\r\n\tSolar radiation is the radiant energy that originated from the sun in the form of electromagnetic radiation at various wavelengths. Solar radiation is the source of renewable energy and can be captured and converted into various forms of energy (e.g. electricity and heat) using different technologies. \r\n\tA very vast amount of solar energy reaches the atmosphere and surface of the earth and solar energy has been used for heating purposes for a very long-time and after solar cells’ invention in 1954, solar cells have also been used widely for electricity generation. Solar cells convert the sunlight into electricity by the creation of voltage and electric current through the so-called photovoltaic effect. \r\n\tPhotovoltaic (PV) solar energy has attracted significant attention in the recent decade as a reliable source for power generation due to various merits such as the free source of energy, abundant materials resources, environmentally friendly and noise-free, longtime service life, requiring low maintenance, technological advancements, market potential, and very importantly, low cost. The growth of using photovoltaic (PV) solar energy as a promising renewable energy technology, is being increased more and more worldwide. Therefore, much further research is needed for possible future developments in the field of solar photovoltaic energy. \r\n\tThe aim of this book is to provide detailed information about solar radiation as the source of photovoltaic (PV) solar energy for a broad range of readership including undergraduate and postgraduate students, young or experienced researchers and engineers. \r\n\tThis should be accomplished by addressing the various technical and practical aspects of solar radiation fundamentals, modeling and the measurement for photovoltaic (PV) solar energy applications. \r\n\tThe majority of this book should describe the basic, modern, and contemporary knowledge and technology of extraterrestrial and terrestrial solar irradiance for photovoltaic (PV) solar energy. \r\n\tThe book covers the most recent developments, innovation and applications concerning the following topics:
\r\n
\r\n\t• Fundamental of solar radiation and photovoltaic solar energy \r\n\t• Solar radiation and photovoltaic solar energy potential \r\n\t• Solar irradiance measurement: techniques, instrumentation and uncertainty analysis \r\n\t• Solar radiation modeling for photovoltaic solar energy applications \r\n\t• Solar monitoring and data quality assessment \r\n\t• Solar resource assessment and photovoltaic system performance \r\n\t• Solar energy and photovoltaic power forecasting
\r\n
\r\n\tThese are accompanied with other useful research topics and material.
",isbn:"978-1-83968-859-1",printIsbn:"978-1-83968-858-4",pdfIsbn:"978-1-83968-860-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"4c3d1319d7286e81bfb15c1f4b20460a",bookSignature:"Dr. Mohammadreza Aghaei",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9862.jpg",keywords:"Solar Radiation Modeling, Solar Data Assessment, Solar Monitoring, Solar Radiation Forecasting, Solar Irradiance Measurements, Solar Instruments, Solar Spectral Distributions, Uncertainty Analysis, Solar Cell Technologies, Photovoltaics (PV), Solar Resource Assessment, Photovoltaics Power Forecasting",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 17th 2020",dateEndSecondStepPublish:"October 15th 2020",dateEndThirdStepPublish:"December 14th 2020",dateEndFourthStepPublish:"March 4th 2021",dateEndFifthStepPublish:"May 3rd 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A senior researcher in the field of photovoltaic solar energy, a postdoctoral scientist at Eindhoven University of Technology (TU/e), Chair of the WG2: reliability and durability of PV in EU COST PEARL PV.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"317230",title:"Dr.",name:"Mohammadreza",middleName:null,surname:"Aghaei",slug:"mohammadreza-aghaei",fullName:"Mohammadreza Aghaei",profilePictureURL:"https://mts.intechopen.com/storage/users/317230/images/system/317230.jpg",biography:"Mohammadreza Aghaei is a senior researcher in the field of photovoltaic solar energy, Eindhoven University of Technology (TU/e), The Netherlands. He is chair of the Working Group 2: reliability and durability of PV in European Cooperation in Science and Technology, COST Action PEARL PV.\nHe received the M.S. degree in electrical engineering from the Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, in 2013, and the Ph.D. degree in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2016.\nHe was a Postdoctoral Scientist with Fraunhofer ISE and Helmholtz-Zentrum Berlin (HZB)-PVcomB, Germany, in 2017 and 2018, respectively. He is a Guest Scientist with the Department of Microsystems Engineering (IMTEK), Solar Energy Engineering, University of Freiburg since 2017. He is currently a Postdoctoral Scientist with the Design of Sustainable Energy Systems Group, Eindhoven University of Technology (TU/e), The Netherlands. He has authored numerous publications in international refereed journals, book chapters, and conference proceedings. The main his research interests include Solar Energy, Photovoltaic systems, PV monitoring, LSC PV, solar cells, machine learning, and UAVs.\nDr. Aghaei is a member of the International Energy Agency, PVPS program-Task 13 and International Solar Energy Society, and also an MC member in EU COST Action PEARL PV.",institutionString:"Eindhoven University of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Eindhoven University of Technology",institutionURL:null,country:{name:"Netherlands"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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\n\t\t\t
1. Introduction
\n\t\t\t
The world is currently highly dependent upon oil for automotive transport. As a result, large amounts of greenhouse gas (GHG) emissions are generated in the passenger automotive sector and are having a substantial effect on the environment. Among the various measures from both the automotive technology side and the transport demand side to reduce energy consumption and GHG emissions, improving the fuel consumption (FC, expressed in litres of gasoline per hundred kilometres of travel [L/100 km]) or fuel economy (FE, usually expressed in [km/L] or miles per gallon [mpg]) of passenger vehicles is regarded as the most effective measure. In this regard, many regions and countries around the world have implemented FC/FE or GHG standards (An et al., 2011), and some — for example, the United States, the European Union, and Japan — are tightening their existing standards.
\n\t\t\t
FC/FE and GHG standards are measured by using chassis dynamometer test cycles, which simulate a variety of driving conditions at typical highway and urban driving speeds in each country and region. However, it is quite well known that a gap exists between FC/FE values generated by dynamometer testing and real-world values worldwide (Schipper & Tax, 1994; Schipper, 2011). Real-world FC/FE values depend strongly upon each driver’s style and location, upon the traffic congestion, weather, and corresponding use of accessories (especially air conditioning), and upon the vehicle’s maintenance condition. No single test cycle can simulate all possible combinations of these factors. Although the energy roadmaps and CO2 reduction targets for the passenger automotive sector in each region and country are based mainly on FC/FE and GHG standards, it is the real-world values that matter. It is doubtful whether reduction targets can be met without more accurate real-world assessments of FC/FE and GHG emissions. Providing more accurate real-world FC/FE values will also better inform consumers of expected fuel costs.
\n\t\t\t
The many studies that have investigated FC/FE and GHG emissions in the real world have used several approaches. Schipper (2011) analysed FC trends for the entire passenger vehicle fleet in the United States, Australia, Japan, and several European countries by using national or regional statistics, as well as the impact of fuel prices upon FC/FE. Wang et al. (2008) explored the influence of driving patterns on FC in China by using a portable emissions measurement system and established an on-road FC estimation model. Duoba et al. (2005) tested the robustness of FE to changes in vehicle activity for hybrid vehicles (HVs) and their counterpart internal combustion engine vehicles by applying various driving schedules upon a chassis dynamometer in the U.S. Several studies have also analysed on-road FC/FE by using information collected by questionnaires or on the internet. Huo et al. (2011) examined the differences between standard test and real-world values for Chinese passenger vehicles by using data voluntarily reported by drivers on the internet; the study gathered 63,115 pieces of real-world FC data for 153 vehicle models. Sagawa & Sakaguchi (2000) analysed the FE of Japanese passenger vehicles using questionnaires, but they were unable to analyse the data with high statistical reliability because of sample number limitations (1,479 samples).
\n\t\t\t
The use of internet-connected mobile phones has become widespread throughout the world, and the range of mobile phone contents and services provided includes those used to track the FC/FE of automobiles. In order to analyse real-world FC/FE in Japan with a high level of statistical reliability, the author’s group put focus upon the FC/FE management service in which voluntarily reported FC log data of the vehicle users are collected through internet-connected mobile phones across Japan and developed and on-road (actual) FC database. The findings for the 24 months from October 2000 to September 2002 are reported by Kudoh et al. (2004).
\n\t\t\t
Since 2000, the number of brand-new passenger vehicles sold in Japan has fluctuated between 4.26 and 4.76 million, which means that about 8% of the total passenger vehicle fleet was replaced with brand-new vehicles annually. Since the FC/FE of brand-new vehicles improved during this period, it is reasonable to conclude that the FC/FE performance of the passenger vehicle fleet itself should also have improved as these new vehicles replaced older ones. In addition, the mobile phone service provider whose log data were used to develop the actual FC database has reported an increase in the number of users in the 2000s.
\n\t\t\t
The author’s group therefore updated the database by extending the data collection period from 24 to 54 months and created a database consisting of 1,645,923 pieces of log data collected from October 2000 through March 2005, including information from 49,677 passenger vehicle users on 2,022 models sold in Japan and conducted a statistical analysis of actual FC for passenger HVs and other passenger vehicles with internal combustion engine in Japan (Kudoh et al., 2007; Kudoh et al., 2008). In addition to the previous achievements of the author’s group, this paper addresses the effects of vehicle specifications towards the actual FC/FE of passenger vehicles in Japan, as derived from the database, from a statistical point of view.
\n\t\t
\n\t\t
\n\t\t\t
2. Reasons for the FC/FE measurement gap
\n\t\t\t
In Japan, targets for FE standards are provided in the revised Law Concerning the Rational Use of Energy (known as the Energy-Saving Law) by implementing the “Top Runners Approach,” which aims to establish energy-efficiency standards that meet or exceed the best energy-efficiency specifications for a product in an industry.
\n\t\t\t
According to the revised law, passenger vehicles sold on the Japanese market in 2010 were expected to achieve the FE standard stipulated in the Japanese 10-15 mode driving schedule for each vehicle inertia weight class. The 10-15 mode driving schedule was developed for exhaust measurement and FE tests of light duty vehicles in Japan, including passenger vehicles; the driving pattern and relationship between velocity and acceleration are shown in Figure 1. The test is conducted on a chassis dynamometer with a hot start at curb weight plus 110 [kg] (the approximate weight of 2 passengers), with the air conditioner and other electrical appliances turned off. Figure 2 shows an example of actual vehicle travel activity measured in an urban area (TMGBE, 1996); the average velocity is almost the same in both figures. Although the 10-15 mode driving schedule is supposed to represent actual vehicle travel activity within Japanese urban areas, acceleration and deceleration occurred more frequently under actual conditions and higher levels of acceleration were observed at low velocities. These factors are thought to be among the main reasons for the gap between 10-15 mode FE and actual FE values.
\n\t\t\t
\n\t\t\t\tFigure 3 depicts the simulated results of the 10-15 mode FC and the actual FC for a passenger gasoline engine vehicle (GV) with a 2,000 cc displacement. The results were calculated under vehicle driving simulation model (Kudoh et al., 2001). At a similar average velocity (as shown in Figure 2), the actual FC was about 13% lower than predicted by the 10-15 mode test. In addition, the actual FC of a vehicle clearly varied according to where it was driven, because the main cause of changes in average velocity is the stop-and-go traffic pattern that occurs frequently in urban areas.
\n\t\t\t
As pointed out by Farrington & Rugh (2000) and Nishio et al. (2008), another important factor that should affect the FC/FE gap is the use of air conditioning, because the air conditioning system is turned off in most test cycles on the chassis dynamometer, including in the Japanese 10-15 mode.
\n\t\t\t
Figure 1.
Japanese 10-15 mode driving schedule.
\n\t\t\t
Figure 2.
An example of actual vehicle travel activity (TMGBE, 1996).
\n\t\t
\n\t\t
Figure 3.
Simulated FC of a passenger vehicle with a 2,000cc gasoline engine.
\n\t\t
\n\t\t\t
3. Outline of the actual FC database
\n\t\t\t
\n\t\t\t\tFigure 4 outlines the actual FC database that the author’s group has been developing based upon the catalogue data of passenger vehicles sold in Japanese market and the voluntarily reported FC log data of vehicle users.
\n\t\t\t
To obtain the passenger vehicle specifications for cars sold in Japan before March 2005, vehicle catalogues for each vehicle name, model year, and model grade were downloaded from an available website on the internet. The vehicle specification database contained information on 35,177 vehicles.
\n\t\t\t
Figure 4.
Outline of the actual FC database.
\n\t\t\t
The actual FC database was developed by using voluntarily reported FC data from vehicle users and the vehicle specification database. The FC data collection system is called e-nenpi (which stands for “electronic FE” in Japanese[1] -); this is an online service for internet-connected mobile phone users[1] - provided by IID, Inc. The system manages information for vehicle owners, including FC performance and recommended routine maintenance. Users of the service register and provide the following information: (1) zip code of residence, (2) vehicle type[1] -, (3) type of engine air intake (turbocharged/supercharged or normal), (4) transmission type (manual or automatic[1] -), and (5) type of fuel used (unleaded gasoline, premium unleaded gasoline, diesel, or liquefied petroleum gas). Through their mobile phone, the user then enters the amount of fuel put into the vehicle’s tank and the odometer reading at the time of fuelling, and the user’s FC data are stored on a server.
\n\t\t\t
The items required for service registration were linked with the vehicle specification database and supplemented with other items such that the following 17 attributes were included in the actual FC database for each user: (1) user ID, (2) base location of where the vehicle was used[1] -, (3) month and year when the vehicle was fuelled, (4) vehicle maker, (5) vehicle name, (6) vehicle type, (7) vehicle class (light passenger vehicle[1] - (LP) or passenger vehicle (P)), (8) type of powertrain (gasoline vehicle (GV), diesel vehicle (DV) or hybrid vehicle (HV)), (9) type of air intake, (10) transmission type, (11) type of drive system (2WD or 4WD), (12) type of fuel injection engine (direct injection or not), (13) whether a variable valve timing system was used, (14) fuel tank capacity, (15) engine displacement, (16) vehicle kerb weight, and (17) 10-15 mode FE.
\n\t\t\t
Although technological specifications may vary within the same vehicle type by grade or model year owing to differences in equipment or improvement in vehicle technologies, the model year of the vehicle owned by each user could not be specified from the log data. Hence, the following values obtained from the vehicle specification database were used in the technological specifications of a vehicle type in the actual FC database: (1) maximum fuel tank capacity, (2) simple average of minimum and maximum vehicle weight, and (3) simple average of minimum and maximum 10-15 mode FE.
\n\t\t\t
A total of 2,937,780 FC log data points was collected over the 54-month study period (from October 2000 through March 2005). Data were excluded under the following conditions to assure the statistical reliability of the database:
\n\t\t\t
a.\twhen the base location of vehicle use could not be specified (21,736 entries);
\n\t\t\t
b.\twhen users specified a vehicle type that was not included in the vehicle specification database (611,357 entries); and
\n\t\t\t
c.\twhen the fuel fill-up rate (γ) was less than 60% or more than 100% (536,620 entries). The rate was calculated as γ = f / C, where f [L] is the amount of fuel put into the tank and C [L] is the fuel tank capacity.
\n\t\t\t
\n\t\t\t\tFE\n\t\t\t\t\n\t\t\t\t\tu,v\n\t\t\t\t [km/L], the FE of user u who owns vehicle type v, was calculated by Equation 1, where d\n\t\t\t\t\n\t\t\t\t\tu,v,i\n\t\t\t\t [km] is driving distance from the last fuelling of the i th data point, f\n\t\t\t\t\n\t\t\t\t\tu,v,i\n\t\t\t\t [L] is the amount of fuel obtained for data point i, and n\n\t\t\t\t\n\t\t\t\t\ti∈u,v\n\t\t\t\t is the number of log data entries.
\n\t\t\t\tFE\n\t\t\t\t\n\t\t\t\t\tv\n\t\t\t\t [km/L], the FE of vehicle type v, was calculated by using Equation 2, where n\n\t\t\t\t\n\t\t\t\t\tu∈v\n\t\t\t\t is the number of users who own v.
Data entries were eliminated from further analysis if they met any of the following conditions:
\n\t\t\t
a.\twhen FE\n\t\t\t\t\n\t\t\t\t\ti∈u,v\n\t\t\t\t was determined to be a statistical outlier by the Grubbs’ test at a critical level of 5% (57,118 entries);
\n\t\t\t
b.\twhen n\n\t\t\t\t\n\t\t\t\t\ti∈u,v\n\t\t\t\t is less than 5 (10,773 entries);
\n\t\t\t
c.\twhen the variance of FE\n\t\t\t\t\n\t\t\t\t\tu∈v\n\t\t\t\t is greater than 10 [(km/L)2] (4,789 entries);
\n\t\t\t
d.\twhen FE\n\t\t\t\t\n\t\t\t\t\tu∈v\n\t\t\t\t was determined to be a statistical outlier by the Grubbs’ test at a critical level of 5% (10,414 entries); and
\n\t\t\t
e.\twhen n\n\t\t\t\t\n\t\t\t\t\tu∈v\n\t\t\t\t is less than 3 (40,050 entries).
\n\t\t\t
After all of the eliminations, 1,645,923 log data points, including pieces of information from 49,677 users and 2,022 vehicle types, were used to develop the actual FC database. A summary of the number of data points, users, and vehicle types is given in Table 1.
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Vehicle type
\n\t\t\t\t\t\t
Number of log data points
\n\t\t\t\t\t\t
Number of users
\n\t\t\t\t\t\t
Number of vehicle types
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Light passenger gasoline vehicle (LP-GV)
\n\t\t\t\t\t\t
< 702 kg
\n\t\t\t\t\t\t
23,563
\n\t\t\t\t\t\t
848
\n\t\t\t\t\t\t
57
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
703 – 827 kg
\n\t\t\t\t\t\t
70,745
\n\t\t\t\t\t\t
2,189
\n\t\t\t\t\t\t
112
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
828 – 1,015 kg
\n\t\t\t\t\t\t
55,655
\n\t\t\t\t\t\t
1,654
\n\t\t\t\t\t\t
93
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,016 – 1,265 kg
\n\t\t\t\t\t\t
1,779
\n\t\t\t\t\t\t
43
\n\t\t\t\t\t\t
3
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Total
\n\t\t\t\t\t\t
151,742
\n\t\t\t\t\t\t
4,734 (0.035%1)
\n\t\t\t\t\t\t
265 (54.6%2)
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Passenger diesel vehicle (P-DV)
\n\t\t\t\t\t\t
1,016 – 1,265 kg
\n\t\t\t\t\t\t
91
\n\t\t\t\t\t\t
4
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,266 – 1,515 kg
\n\t\t\t\t\t\t
496
\n\t\t\t\t\t\t
19
\n\t\t\t\t\t\t
5
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,516 – 1,765 kg
\n\t\t\t\t\t\t
8,040
\n\t\t\t\t\t\t
236
\n\t\t\t\t\t\t
27
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,766 – 2,015 kg
\n\t\t\t\t\t\t
28,021
\n\t\t\t\t\t\t
809
\n\t\t\t\t\t\t
57
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2,016 – 2,265 kg
\n\t\t\t\t\t\t
18,159
\n\t\t\t\t\t\t
477
\n\t\t\t\t\t\t
22
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2,266 kg +
\n\t\t\t\t\t\t
188
\n\t\t\t\t\t\t
7
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Total
\n\t\t\t\t\t\t
54,995
\n\t\t\t\t\t\t
1,552 (0.061%1)
\n\t\t\t\t\t\t
113 (21.4%2)
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Passenger gasoline vehicle (P-GV)
\n\t\t\t\t\t\t
< 702 kg
\n\t\t\t\t\t\t
1,179
\n\t\t\t\t\t\t
48
\n\t\t\t\t\t\t
4
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
703 – 827 kg
\n\t\t\t\t\t\t
10,626
\n\t\t\t\t\t\t
380
\n\t\t\t\t\t\t
20
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
828 – 1,015 kg
\n\t\t\t\t\t\t
120,105
\n\t\t\t\t\t\t
4,005
\n\t\t\t\t\t\t
169
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,016 – 1,265 kg
\n\t\t\t\t\t\t
346,834
\n\t\t\t\t\t\t
10,968
\n\t\t\t\t\t\t
481
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,266 – 1,515 kg
\n\t\t\t\t\t\t
600,790
\n\t\t\t\t\t\t
17,468
\n\t\t\t\t\t\t
567
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,516 – 1,765 kg
\n\t\t\t\t\t\t
281,517
\n\t\t\t\t\t\t
8150
\n\t\t\t\t\t\t
285
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,766 – 2,015 kg
\n\t\t\t\t\t\t
51,055
\n\t\t\t\t\t\t
1543
\n\t\t\t\t\t\t
84
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2,016 – 2,265 kg
\n\t\t\t\t\t\t
18,398
\n\t\t\t\t\t\t
526
\n\t\t\t\t\t\t
24
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2,266 kg +
\n\t\t\t\t\t\t
3298
\n\t\t\t\t\t\t
87
\n\t\t\t\t\t\t
2
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Total
\n\t\t\t\t\t\t
143,3802
\n\t\t\t\t\t\t
43,175 (0.108%1)
\n\t\t\t\t\t\t
1,636 (41.3%2)
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Passenger (gasoline) hybrid vehicle (P-HV)
\n\t\t\t\t\t\t
703 – 827 kg
\n\t\t\t\t\t\t
66
\n\t\t\t\t\t\t
4
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
828 – 1,015 kg
\n\t\t\t\t\t\t
379
\n\t\t\t\t\t\t
12
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,016 – 1,265 kg
\n\t\t\t\t\t\t
2,455
\n\t\t\t\t\t\t
86
\n\t\t\t\t\t\t
3
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,266 – 1,515 kg
\n\t\t\t\t\t\t
671
\n\t\t\t\t\t\t
43
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,766 – 2,015 kg
\n\t\t\t\t\t\t
1,447
\n\t\t\t\t\t\t
51
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2,016 – 2,265 kg
\n\t\t\t\t\t\t
366
\n\t\t\t\t\t\t
20
\n\t\t\t\t\t\t
1
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Total
\n\t\t\t\t\t\t
5,384
\n\t\t\t\t\t\t
216 (0.111%1)
\n\t\t\t\t\t\t
8 (57.1%2)
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Total
\n\t\t\t\t\t\t
1,645,923
\n\t\t\t\t\t\t
49,677 (0.089%1)
\n\t\t\t\t\t\t
2,022 (40.5%2)
\n\t\t\t\t\t
\n\t\t\t\t
Table 1.
Data size categories of the actual FC database. The vehicle weight class follows the Japanese inertia weight classes for passenger vehicles. 1 Sampling rate relative to the number of vehicles owned as of March 2005. 2 Sampling rate relative to the number of vehicle types included in the vehicle specification database.
\n\t\t\t
Although Equations 1 and 2 assume that users fill their tanks to the same (full) level at every refuelling, there may be users who do not do so. The e-nenpi system recommends that registered users fill up the vehicle tank, and a confirmation message to check whether they have filled up the tank is shown when they input their fuel log through the mobile phone. The second and subsequent log data entries were saved in the server only after a user had confirmed filling up more than twice. In addition, some data were eliminated if they did not satisfy criterion c; the average of fuel fill-up rate of the remaining log data was 76.8% (standard deviation = 8.82%). Users should refuel before the tank was completely empty, indicating that most of the user data included in the actual FC database were acquired as the users filled up at petrol stations, so the fill level of the vehicles was expected to be almost the same every time.
\n\t\t
\n\t\t
\n\t\t\t
4. Vehicle specifications and actual FC/FE
\n\t\t\t
In the Japanese passenger vehicle market, 12 HV types had been launched as of March 2005; 8 were included in the actual FC database. It is assumed that the FC/FE performance of these vehicles would vary with differences in the powertrain configuration (e.g., series hybrid, parallel hybrid, or power-split hybrid) or degree of hybridisation (such as full hybrid, power-assist hybrid, mild hybrid, or plug-in hybrid). However, because of the difficulties involved in including all of these factors with a high level of statistical reliability, the passenger HV types were combined in this study.
\n\t\t\t
\n\t\t\t\t
4.1. Japanese 10-15 mode and actual FE
\n\t\t\t\t
\n\t\t\t\t\tFigure 5 depicts the relationship between the Japanese 10-15 mode FE and actual FE. FE\n\t\t\t\t\t\n\t\t\t\t\t\tv,actual\n\t\t\t\t\t [km/L], the actual FE of vehicle type v, was calculated from Equation 3 (USEPA 2010), where d\n\t\t\t\t\t\n\t\t\t\t\t\tv,i\n\t\t\t\t\t and f\n\t\t\t\t\t\n\t\t\t\t\t\tv,i\n\t\t\t\t\t are driving distance [km] and amount of fuel [L] at i th log data point of vehicle v.
\n\t\t\t\t\tTable 2 shows the results of a linear regression analysis and the 95% confidential interval (95 CI) described by Equation 4, where FE\n\t\t\t\t\t\n\t\t\t\t\t\tv,10-15\n\t\t\t\t\t [km/L] is the 10-15 mode FE of vehicle\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tv\n\t\t\t\t\t\t\n\t\t\t\t\t.
If a plotted point was on the diagonal line shown in Figure 5, the actual FE of the vehicle was exactly the same as 10-15 mode FE. As can be seen in the figure, the gap between 10-15 mode FE and actual FE increased as the 10-15 mode FE increased.
Estimates of parameters by Equation 4. t is the t statistics.
\n\t\t\t\t
25 P-GVs had an actual FE that was higher than the corresponding 10-15 mode FE. (These are above the line in Figure 5.) Figure 6 shows the achievement ratio of actual FE to 10-15 mode FE of domestically produced and imported P-GVs; 23 out of the 25 P-GVs with a ratio of greater than 1 were imported vehicles. These results indicate that the achievement ratio of actual FE to 10-15 mode FE may be higher for imported vehicles than for domestically produced vehicles. The results of a two-tailed Welch test confirmed that the mean achievement ratios of domestically produced passenger vehicles (x) were significantly lower than those of imported passenger vehicles (y) (mean of x = 0.758, variance of x = 0.00445, mean of y = 0.854, variance of y = 0.00810, T = 15.1, degree of freedom = 268; p < 0.05). One possible explanation is that the drivetrains or transmissions of imported vehicles are not optimised for Japanese road conditions and their 10-15 mode FEs tend to be lower than their counterpart domestically produced P-GVs.
\n\t\t\t
\n\t\t\t
Figure 6.
Comparison of domestically produced P-GVs and imported P-GVs.
\n\t\t\t
\n\t\t\t\t
4.2. Vehicle weight and actual FC
\n\t\t\t\t
Weight-saving technologies in passenger vehicles will play an important role in improving FC, along with improvements in engine and drivetrain efficiency. Figure 7 depicts the relationship between vehicle weight and actual FC. Here, FC\n\t\t\t\t\t\n\t\t\t\t\t\tv,actual\n\t\t\t\t\t [L/100 km], the actual FC of vehicle type v, is calculated by Equation 5.
Two FC standards are shown in Figure 7: the Japanese 2010 standard for GVs and the 2005 standard for DVs. Points plotted above the two lines represent vehicles that do not achieve the FC standards in the real world. Although most brand-new passenger vehicles were announced to have achieved the FC standard by 2005, Figure 7 reveals that only some P-DVs and all the P-HVs achieved the Japanese FC standard in the real world at that time.
\n\t\t\t\t
Since FC\n\t\t\t\t\t\n\t\t\t\t\t\tv,actual\n\t\t\t\t\t can be thought to be proportional to vehicle weight w [kg], a linear regression analysis was conducted by using Equation 6 (Table 3).
The analysis showed that it is difficult to explain the FC of LP-GVs and P-DVs only by vehicle weight. Sales of brand-new LP-GVs, which are restricted in terms of vehicle size and engine displacement, are rapidly expanding in Japan, and Japanese vehicle makers provide a variety of vehicle types (e.g., hatchbacks and wagons) within the regulatory standard. To compensate for the increase in vehicle weight incurred by equipment installed to meet consumer needs or to satisfy safety standards, many LP-GVs use turbochargers. Including only LP-GVs that were introduced to the market after 1998 (when the LP vehicle standards were changed to meet new crash safety standards), the engine displacement of LP-GVs were from 657 – 660 [cc] but their average vehicle weight was 842 [kg] with a wide variation from 550 and 1,060 kg. As a result, the FC differs owing to differences in running resistance (attributed mainly to differences in vehicle shape), transmission type, drive system, and turbocharging, which result in the low R\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t value (0.471).
Estimates of parameters by Equation 6. n is sample number, B is partial regression coefficient and t is t statistics, respectively.
\n\t\t\t\t
Of the 113 P-DVs plotted in Figure 7, 25 are 4WD, 92 have AT/CVT transmission, 108 are turbocharged, and 11 have a direct injection engine. The vehicle weight range of 1,705—2,165 [kg] is small compared with that of P-GVs (715—2,380 [kg]). The low R\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t value (0.336) for P-DVs indicate that it is difficult to explain actual FC only with vehicle weight, for the actual FC of a vehicle varies by the combinations of various vehicle specifications.
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
4.3. Effect of vehicle technologies on actual FC of gasoline-fuelled passenger vehicles
\n\t\t\t\t
A multiple regression analysis was conducted to evaluate the effect of vehicle technologies on the actual FC of gasoline-fuelled passenger vehicles (P-GVs and P-HVs). A P-GV with a manual transmission and 2WD was set as the baseline. The regression equation can be described as Equation 7:
where w is vehicle weight [kg] and D\n\t\t\t\t\t\n\t\t\t\t\t\tHV\n\t\t\t\t\t, D\n\t\t\t\t\t\n\t\t\t\t\t\tAT/CVT\n\t\t\t\t\t, D\n\t\t\t\t\t\n\t\t\t\t\t\tTC\n\t\t\t\t\t, D\n\t\t\t\t\t\n\t\t\t\t\t\t4WD\n\t\t\t\t\t, D\n\t\t\t\t\t\n\t\t\t\t\t\tDI\n\t\t\t\t\t, and D\n\t\t\t\t\t\n\t\t\t\t\t\tVVT\n\t\t\t\t\t are the dummy variables for P-HV, transmission (AT/CVT), turbocharging (TC), 4WD, direct injection (DI), and variable valve timing (VVT), respectively. The parameter estimates are summarized in Table 4.
\n\t\t\t\t
Figure 7.
Vehicle weight and actual FC
\n\t\t\t\t
Using the estimates shown in Table 4 and Equation 7, it is confirmed that the use of direct injection and variable valve timing led to a decrease in actual FC, whereas the use of an automatic transmission and turbocharging resulted in an increase in actual FC. Although the partial regression coefficients of HV and 4WD are negative, adding hybrid technology and 4WD to a baseline P-GV increased vehicle weight. Hence, to evaluate the effect of hybridisation and 4WD, the balance between vehicle weight increase and the coefficients of the dummy variables given in Table 4 should be considered.
\n\t\t\t\t
Among the 8 HV models included in the actual FC database, 3 models also had equivalent GVs within the same vehicle name, 3 had engines that were variants of the GV models, and 2 were dedicated HV models. Therefore, counterpart GV models could be defined for 6 of the 8 HV models. Although the vehicle weight of HVs depends upon various vehicle specifications, the weight increase of these 6 HVs from their counterpart GVs ranged from 40 to 195 [kg]. Equation 7 and Table 4 were then used to estimate a 0.336—1.64 [L/100km] increase in actual FC from hybridisation. Because the actual FC improvement effect evaluated from the partial regression coefficient of HV prevailed in this estimate, however, it is estimated that hybridisation contributed to an actual FC improvement (-4.44 to -3.14 [L/100km]) from the baseline P-GV.
\n\t\t\t\t
Of the 1,615 samples analysed in this section, 370 had the same vehicle name and model year for both 2WD and 4WD models (other specifications, such as transmission type, turbocharging, and direct injection, were the same). The use of 4WD increased weight by 83.1 [kg] on average (standard deviation = 38.8 [kg]). The partial regression coefficients d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t and d\n\t\t\t\t\t\n\t\t\t\t\t\t5\n\t\t\t\t\t shown in Table 4 indicate that a weight increase of 83.1 kg would result in an actual FC increase of 0.329 [L/100km].
Estimates of parameters by Equation 7. n is sample number, B is partial regression coefficient and t is t statistics, respectively.
\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
5. Annual differences in mean actual FC of gasoline-fuelled passenger vehicles
\n\t\t\t
Annual (fiscal year, FY) changes in vehicle weight and actual FC for gasoline-fuelled passenger vehicles from FY 2001 to 2004 are analysed. Table 5 presents the descriptive statistics of vehicle weight for gasoline-fuelled passenger vehicles (P-GVs and P-HVs) that were used to conduct a one-factor analysis of variance. No significant difference was observed for mean vehicle weight of P-HVs (F = 0.252, p = 0.859), but a significant difference was found for P-GVs (F = 2.71, p = 0.044). Therefore, a post-hoc multiple comparison by Sheffé’s test on vehicle weight of P-GVs was conducted, but no significant differences were observed.
\n\t\t\t
Similarly, the mean differences of actual FC are tested. As shown in Section 4.2, actual FC is presented as proportional to vehicle weight; therefore, an analysis of covariance was carried out to adjust for the effect of vehicle weight in actual FC. Mean actual FC of P-GVs decreased significantly from FY2001 until FY2004 (F = 19.7, p = 0.000). Post-hoc multiple comparisons with the Sidak adjustment showed that the mean actual FC values adjusted for vehicle weight were significantly different, except between FY2003 and FY2004 (Table 6). No significant differences were observed for P-HVs (F = 0.299, p = 0.826).
\n\t\t\t
The results indicate that the actual FC of P-GVs included in the actual FC database steadily improved, most likely as a result of an increase in the number of vehicles equipped with FC-improving technologies and not because of weight reductions. The lack of significant changes for P-HVs can be attributed to the fact that only small numbers of new-type P-HVs had entered the Japanese passenger vehicle fleet at the time of the study and also to a lack of drastic improvements in the P-HVs produced during this period.
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
FY
\n\t\t\t\t\t\t
P-GV
\n\t\t\t\t\t\t
P-HV
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tn\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tμ\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tσ\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tn\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tμ\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tσ\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2001
\n\t\t\t\t\t\t
970
\n\t\t\t\t\t\t
1,330.92
\n\t\t\t\t\t\t
268.04
\n\t\t\t\t\t\t
5
\n\t\t\t\t\t\t
1,196.00
\n\t\t\t\t\t\t
415.73
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2002
\n\t\t\t\t\t\t
1,073
\n\t\t\t\t\t\t
1,342.16
\n\t\t\t\t\t\t
275.34
\n\t\t\t\t\t\t
4
\n\t\t\t\t\t\t
1,290.00
\n\t\t\t\t\t\t
414.17
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2003
\n\t\t\t\t\t\t
1,091
\n\t\t\t\t\t\t
1,353.79
\n\t\t\t\t\t\t
272.29
\n\t\t\t\t\t\t
7
\n\t\t\t\t\t\t
1,378.57
\n\t\t\t\t\t\t
410.15
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
2004
\n\t\t\t\t\t\t
1,089
\n\t\t\t\t\t\t
1,362.95
\n\t\t\t\t\t\t
269.70
\n\t\t\t\t\t\t
7
\n\t\t\t\t\t\t
1,378.57
\n\t\t\t\t\t\t
410.15
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
All
\n\t\t\t\t\t\t
4,223
\n\t\t\t\t\t\t
1,347.94
\n\t\t\t\t\t\t
271.70
\n\t\t\t\t\t\t
23
\n\t\t\t\t\t\t
1,323.48
\n\t\t\t\t\t\t
390.39
\n\t\t\t\t\t
\n\t\t\t\t
Table 5.
Descriptive statistics of vehicle weight [kg]. n is the number of vehicle types, μ is the population mean, and σ is standard deviation.
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
[L/100km]
\n\t\t\t\t\t\t
FY2002
\n\t\t\t\t\t\t
FY2003
\n\t\t\t\t\t\t
FY2004
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
FY2001
\n\t\t\t\t\t\t
0.190*
\n\t\t\t\t\t\t
0.403*
\n\t\t\t\t\t\t
0.441*
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
FY2002
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.213*
\n\t\t\t\t\t\t
0.251*
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
FY2003
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.038
\n\t\t\t\t\t
\n\t\t\t\t
Table 6.
Results of post-hoc multiple comparisons using the Sidak adjustment for the mean actual FC of P-GVs. The value in each cell shows the differential in the population mean μ\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tr\n\t\t\t\t\t\t in rowwise group r and μ\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tc\n\t\t\t\t\t\t in columnwise group c. For example, μ\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFY2001\n\t\t\t\t\t\t – μ\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFY2002\n\t\t\t\t\t\t = 0.190. Asterisk denotes significance at 5% level.
\n\t\t
\n\t\t
\n\t\t\t
6. Validity of actual FC obtained from the actual FC database
\n\t\t\t
To check the validity of the actual FC values calculated from the database, two cases from the database were compared with a third that was calculated from published statistics for gasoline-fuelled passenger vehicles (P-GVs and P-HVs):
\n\t\t\t
Case A: The actual FC of gasoline-fuelled passenger vehicles was estimated for each FY directly from the database.
\n\t\t\t
Case B: The actual FC of gasoline-fuelled passenger vehicles was estimated from the results of the regression analysis between vehicle weight and actual FC (Table 7, Equation 6) and the estimated number of vehicles owned, by vehicle weight (by 10 kg increments), for each FY.
\n\t\t\t
Case C: The actual FC of gasoline-fuelled passenger vehicles was estimated from national statistics (MLIT, 2003–2005).
\n\t\t\t
For Case B, the number of vehicles owned by vehicle weight was estimated from the vehicle specification database and various published statistics (AIRIA1, 2003–2005; AIRIA2, 2003–2005; AIRIA3, 2003–2005). Figure 8 shows the ownership rate (OR) relative to the total number of vehicles owned, by Japanese inertia weight class, for passenger vehicles from FY2002 (March 2003) to FY2004 (March 2005). The sampling rate (SR) — the number of vehicles actually included in the estimates of Case A as a ratio of the total number of vehicles owned — is also shown in the figure. The vehicle weight distribution of the database (SR) does not reflect the real-world distribution (OR); OR has a normal distribution, whereas SR is higher for both light (< 702 [kg]) and heavy (1,766+ [kg]) vehicles. Therefore, actual FC values compiled directly from the database in Case A might have been biased as a result of the vehicle weight distribution.
\n\t\t\t
Figure 8.
Ownership rates of gasoline-fuelled passenger vehicles and sampling rates of vehicles included in the database.
\n\t\t\t
As described in Section 4, the mean actual FC adjusted by vehicle weight improved each year in the study period. Therefore, Case B was designed to reflect improvements in actual FC adjusted for the vehicle weight bias that might have been included in the database (Case A). Because no significant improvement in actual FC was observed for P-HVs from FY2002 to FY2004, the results of the regression analysis shown in Table 3 were used for P-HVs; the results from Table 7 were used for P-GVs.
\n\t\t\t
\n\t\t\t\tTable 8 shows the estimates of actual FC of gasoline-fuelled passenger vehicles for the three cases from FY2002 to FY2004. The actual FC steadily improved from FY2002 to FY2004 in Cases A and C, but the actual FC did not improve from FY2003 and FY2004 in Case B, similar to the results in the same time period shown in Table 6. Although there are small differences in each FY, the estimates of actual FC of gasoline-fuelled passenger vehicles in Cases A and B were within 4% of the Case C estimates in all instances. Therefore, the actual FC values derived from the database appear to be compatible with the estimates from published statistics.
Estimates of parameters by Equation 4 for P-GVs for FY2002–2004. n is sample number, B is partial regression coefficient and t is t statistics, respectively.
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Case or comparison
\n\t\t\t\t\t\t
FY2002
\n\t\t\t\t\t\t
FY2003
\n\t\t\t\t\t\t
FY2004
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Case A
\n\t\t\t\t\t\t
11.81
\n\t\t\t\t\t\t
11.62
\n\t\t\t\t\t\t
11.59
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Case B (95CI)
\n\t\t\t\t\t\t
11.36 (11.24 – 11.48)
\n\t\t\t\t\t\t
11.23 (11.10 – 11.36)
\n\t\t\t\t\t\t
11.24 (11.11 – 11.37)
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Case C
\n\t\t\t\t\t\t
11.66
\n\t\t\t\t\t\t
11.49
\n\t\t\t\t\t\t
11.13
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Case A / Case C
\n\t\t\t\t\t\t
1.01
\n\t\t\t\t\t\t
1.01
\n\t\t\t\t\t\t
1.04
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Case B / Case C (95 CI)
\n\t\t\t\t\t\t
0.974 (0.964 – 0.984)
\n\t\t\t\t\t\t
0.977 (0.966 – 0.988)
\n\t\t\t\t\t\t
1.01 (0.998 – 1.02)
\n\t\t\t\t\t
\n\t\t\t\t
Table 8.
Comparison of actual FC [L/100km] of gasoline-fuelled passenger vehicles for Cases A–C.
\n\t\t
\n\t\t
\n\t\t\t
7. Conclusion
\n\t\t\t
In order to quantify the relationship between vehicle specifications and actual FC with statistical reliability, an actual FC database was developed by using vehicle specification data and voluntarily reported data collected from an internet-connected mobile phone system throughout Japan. The database was used to conduct statistical analyses to evaluate the effects of various vehicle specifications on the FC/FE of passenger vehicles. The actual FC adjusted by vehicle weight was shown to have significantly improved from FY2001 to FY 2004. Moreover, estimates of the actual FC of gasoline-fuelled passenger vehicles obtained from the database were consistent with estimates calculated from national statistics.
\n\t\t\t
With the revision of the Energy-Saving Law in July 2007, Japan changed from using the 10-15 mode to the JC08 mode (UNEP, 2012); the new 2015 FE standards for passenger vehicles are based on the Top Runners Approach provided in the JC08 mode. Japanese vehicle makers have already started to sell new passenger vehicles that have achieved the 2015 FE standard, so the effects of equipping vehicles with various types of new and more fuel efficient technologies may influence the actual FC of these vehicles as well. The author’s group plans to extend the data collection period presented in this paper and to update the actual FC database to reflect state-of-the-art vehicle technologies in the real world.
\n\t\t\t
Finally, the World Forum for Harmonization of Vehicle Regulations, which is a working party (WP.29) of the United Nations Economic Commission for Europe, has decided to set up an informal group under its Working Party on Pollution and Energy to develop a worldwide harmonized light duty test cycle (the Worldwide Harmonized Light Duty Vehicle Test Procedures, WLTP) by 2013. This cycle will represent typical driving conditions around the world (UNECE, 2012). Because the actual FC/FE of vehicles might show different trends if the WLTP is adopted and applied to meet new FC/FE standards, the movement towards the endorsement of the WLTP could influence future studies as well.
\n\t\t
\n\t\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/41021.pdf",chapterXML:"https://mts.intechopen.com/source/xml/41021.xml",downloadPdfUrl:"/chapter/pdf-download/41021",previewPdfUrl:"/chapter/pdf-preview/41021",totalDownloads:2840,totalViews:122,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"November 20th 2011",dateReviewed:"May 18th 2012",datePrePublished:null,datePublished:"November 14th 2012",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/41021",risUrl:"/chapter/ris/41021",book:{slug:"internal-combustion-engines"},signatures:"Yuki Kudoh",authors:[{id:"141922",title:"Dr.",name:"Yuki",middleName:null,surname:"Kudoh",fullName:"Yuki Kudoh",slug:"yuki-kudoh",email:"kudoh.yuki@aist.go.jp",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Reasons for the FC/FE measurement gap",level:"1"},{id:"sec_3",title:"3. Outline of the actual FC database",level:"1"},{id:"sec_4",title:"4. Vehicle specifications and actual FC/FE",level:"1"},{id:"sec_4_2",title:"4.1. Japanese 10-15 mode and actual FE",level:"2"},{id:"sec_5_2",title:"4.2. Vehicle weight and actual FC",level:"2"},{id:"sec_6_2",title:"4.3. Effect of vehicle technologies on actual FC of gasoline-fuelled passenger vehicles",level:"2"},{id:"sec_8",title:"5. Annual differences in mean actual FC of gasoline-fuelled passenger vehicles",level:"1"},{id:"sec_9",title:"6. Validity of actual FC obtained from the actual FC database",level:"1"},{id:"sec_10",title:"7. 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Number of Vehicles Owned by Vehicle Weight by Vehicle Type as of March Each Year, (in Japanese)\n\t\t\t'},{id:"B5",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDuoba\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLohse-Bush\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBohn\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2005 Investigating Vehicle Fuel Economy Robustness of Conventional and Hybrid Electric Vehicles, Proceedings on the 21st Worldwide Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition, Monaco, April 2005\n\t\t\t'},{id:"B6",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFarrington\n\t\t\t\t\t\t\tR.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRugh\n\t\t\t\t\t\t\tJ. .\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tOctober\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2000\n\t\t\t\t\tImpact of Vehicle Air-Conditioning on Fuel Economy, Tailpipe Emissions, and Electric Vehicle Range: Reprint, National Renewable Energy Laboratory, Retrieved from <http://www.nrel.gov/docs/fy00osti/28960.pdf>\n\t\t\t\t\n\t\t\t'},{id:"B7",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHuo\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYao\n\t\t\t\t\t\t\tZ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHe\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYu\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2011 (2011) Fuel Consumption Rates of Passenger Cars in China: Labels Versus Real-world. Energy Policy, 39\n\t\t\t\t\t11 (November 2010), 7130\n\t\t\t\t\t7135 , 0301-4215\n\t\t\t\t\n\t\t\t'},{id:"B8",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKudoh\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tIshitani\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMatsuhashi\n\t\t\t\t\t\t\tR.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYoshida\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMorita\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKatsuki\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKobayashi\n\t\t\t\t\t\t\tO.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2001 Environmental Evaluation of Introducing Electric Vehicles Using a Dynamic Traffic Flow Model, Applied Energy, 69\n\t\t\t\t\t2 (June 2001), 145\n\t\t\t\t\t159 , 0306-2619\n\t\t\t\t\n\t\t\t'},{id:"B9",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKudoh\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKondo\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMatsuhashi\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKobayashi\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMoriguchi\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2004 Current status of actual fuel-consumptions of petrol-fuelled passenger vehicles in Japan, Applied Energy, 79\n\t\t\t\t\t3 (November 2004), 291\n\t\t\t\t\t308 , 0306-2619\n\t\t\t\t\n\t\t\t'},{id:"B10",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKudoh\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMatsuhashi\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKondo\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKobayashi\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMoriguchi\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYagita\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007 Statistical Analysis of Fuel Consumption of Hybrid Electric Vehicles in Japan, The World Electric Vehicle Association Journal, 1\n\t\t\t\t\t142\n\t\t\t\t\t147 , 2032-6653\n\t\t\t\t\n\t\t\t'},{id:"B11",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKudoh\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMatsuhashi\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKondo\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKobayashi\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMoriguchi\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYagita\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008 Statistical Analysis on the Transition of Actual Fuel Consumption by Improvement of Japanese 10•15 Mode Fuel Consumption, Journal of the Japan Institute of Energy, 87\n\t\t\t\t\t11 (November 2008), 930\n\t\t\t\t\t937 , 0916-8753 (in Japanese)\n\t\t\t'},{id:"B12",body:'\n\t\t\t\t\n\t\t\t\t\tMinistry of Land, Transport and Infrastructures (MLIT) (2003-2005).\n\t\t\t\t\tAnnual Statistics of Automobile Transport, (in Japanese)\n\t\t\t'},{id:"B13",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tNishio\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKaneko\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMurata\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDaisho\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSakai\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSuzuki\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008 Consideration of Evaluation for Fuel Consumption under using Air Conditioner, Transactions of Society of Automotive Engineers of Japan, Society of Automotive Engineers of Japan, 39\n\t\t\t\t\t6 (November 2008), 6 229-6_234, 0287-8321 (in Japanese)\n\t\t\t'},{id:"B14",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSagawa\n\t\t\t\t\t\t\tN.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSakaguchi\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2000 Possibility of introducing fuel-efficient vehicles and fuel consumption trends of passenger vehicles, Proceedings on the 16th Conference on Energy System, Economy, and the Environment, Japan Society of Energy and Resources, Tokyo, January 2000, (in Japanese).\n\t\t\t'},{id:"B15",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchipper\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tTax\n\t\t\t\t\t\t\tW.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1994 New car test and actual fuel economy: yet another gap? Transport Policy, 1\n\t\t\t\t\t4 (October 1994), 257\n\t\t\t\t\t265 , 0096-7070X\n\t\t\t'},{id:"B16",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchipper\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2011 Automobile use, fuel economy and CO2 emissions in industrialized countries: Encouraging trends through 2008? Transport Policy, 18\n\t\t\t\t\t2 (March 2011), 358\n\t\t\t\t\t372 , 0096-7070X\n\t\t\t'},{id:"B17",body:'\n\t\t\t\t\n\t\t\t\t\tTokyo Metropolitan Government Bureau of Environment (TMGBE)\n\t\t\t\t\t1996\n\t\t\t\t\tInvestment Report of Traffic Volume and Exhaust Gases from Vehicles (Outline), (in Japanese).\n\t\t\t'},{id:"B18",body:'\n\t\t\t\t\n\t\t\t\t\tUnited Nations Economic Commission for Europe (UNECE) (n.d.\n\t\t\t\t\t2012 Working Party on Pollution and Energy (GPRE), In: UNECE, 28.03.2012, Available from: < http://www.unece.org/trans/main/wp29/meeting_docs_grpe.html>\n\t\t\t\t\n\t\t\t'},{id:"B19",body:'\n\t\t\t\t\n\t\t\t\t\tUnited Nations Environment Programme (UNEP)\n\t\t\t\t\t2012 Japanese Test Cycles, In: Cleaner, More Efficient Vehicles, 28.03.2012, Available from: <http://www.unep.org/transport/gfei/autotool/approaches/information/test_cycles.asp#Japanese>\n\t\t\t\t\n\t\t\t'},{id:"B20",body:'\n\t\t\t\t\n\t\t\t\t\tUnited States Environmental Protection Agency (USEPA)\n\t\t\t\t\t2010\n\t\t\t\t\tLight-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 through 2010, USEPA, Retrieved from <http://www.epa.gov/otaq/cert/mpg/fetrends/420r10023.pdf>\n\t\t\t\t\n\t\t\t'},{id:"B21",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWang\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFu\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tZhou\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLi\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008 Modelling of the fuel consumption for passenger cars regarding driving characteristics, Transportation Research Part D: Transport and Environment, 13\n\t\t\t\t\t7 (October 2008), 479\n\t\t\t\t\t482, 1361-9209\n\t\t\t\t\n\t\t\t'}],footnotes:[{id:"fn1",explanation:"More information is available at: http://e-nenpi.com (in Japanese)."},{id:"fn2",explanation:"Although the service was originally provided only for internet-connected mobile phone users, the provider currently offers the service for personal computers as well."},{id:"fn3",explanation:"Vehicle type is a code prepared by vehicle makers and approved by the government for vehicles sold and used in Japan to identify each vehicle."},{id:"fn4",explanation:"Although the FC may differ depending on the type of automatic transmission, they are grouped together within the database owing to data restrictions."},{id:"fn5",explanation:"This was determined from the zip code provided by the registered user."},{id:"fn6",explanation:"A light passenger vehicle is equivalent to, or smaller than, the EU’s A-segment. Its physical size and engine power are regulated as follows: maximum length, 3.39 [m]; maximum width, 1.48 [m]; maximum height, 2 [m]; maximum engine displacement, 660 [cc]; and maximum engine power, 64 [hp]."}],contributors:[{corresp:null,contributorFullName:"Yuki Kudoh",address:null,affiliation:'
'}],corrections:null},book:{id:"2455",title:"Internal Combustion Engines",subtitle:null,fullTitle:"Internal Combustion Engines",slug:"internal-combustion-engines",publishedDate:"November 14th 2012",bookSignature:"Kazimierz Lejda and Pawel Wos",coverURL:"https://cdn.intechopen.com/books/images_new/2455.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"10571",title:"Prof.",name:"Kazimierz",middleName:null,surname:"Lejda",slug:"kazimierz-lejda",fullName:"Kazimierz Lejda"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"41015",title:"Factors Determing Ignition and Efficient Combustion in Modern Engines Operating on Gaseous Fuels",slug:"factors-determing-ignition-and-efficient-combustion-in-modern-engines-operating-on-gaseous-fuels",totalDownloads:6986,totalCrossrefCites:0,signatures:"Wladyslaw 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P. Ashok",authors:[{id:"118366",title:"Dr.",name:"M.P.",middleName:null,surname:"Ashok",fullName:"M.P. Ashok",slug:"m.p.-ashok"}]},{id:"35628",title:"Multivariate Modeling in Quality Control of Viscosity in Fuel: An Application in Oil Industry",slug:"multivariate-modeling-in-quality-control-of-viscosity-in-fuel-an-application-in-oil-industry",signatures:"Leandro Valim de Freitas, Fernando Augusto Silva Marins, Ana Paula Barbosa Rodrigues de Freitas, Messias Borges Silva and Carla Cristina Almeida Loures",authors:[{id:"131093",title:"Ph.D. Student",name:"Leandro",middleName:"Valim De",surname:"Freitas",fullName:"Leandro Freitas",slug:"leandro-freitas"},{id:"136723",title:"Dr.",name:"Messias",middleName:null,surname:"Borges Silva",fullName:"Messias Borges Silva",slug:"messias-borges-silva"},{id:"137460",title:"Dr.",name:"Fernando",middleName:null,surname:"Marins",fullName:"Fernando Marins",slug:"fernando-marins"},{id:"138047",title:"MSc.",name:"Carla",middleName:null,surname:"Loures",fullName:"Carla Loures",slug:"carla-loures"},{id:"140849",title:"MSc.",name:"Ana Paula",middleName:null,surname:"Freitas",fullName:"Ana Paula Freitas",slug:"ana-paula-freitas"}]},{id:"35629",title:"Experimental Investigation on Premixed Combustion in a Diesel Engine with Ultra-Multihole Nozzle",slug:"experimental-investigation-on-premixed-combustion-in-a-diesel-engine-with-ultra-multihole-nozzle-",signatures:"Xuelong Miao,Yusheng Ju,Xianyong Wang, Jianhai Hong and Jinbao Zheng",authors:[{id:"113488",title:"Dr.",name:"Xuelong",middleName:null,surname:"Miao",fullName:"Xuelong Miao",slug:"xuelong-miao"}]},{id:"35630",title:"Simulation of Combustion Process in Direct Injection Diesel Engine Based on Fuel Injection Characteristics",slug:"simulation-of-combustion-process-in-direct-injection-diesel-engine-based-on-fuel-injection-character",signatures:"Kazimierz Lejda and Paweł Woś",authors:[{id:"10571",title:"Prof.",name:"Kazimierz",middleName:null,surname:"Lejda",fullName:"Kazimierz Lejda",slug:"kazimierz-lejda"},{id:"119441",title:"Ph.D.",name:"Paweł",middleName:null,surname:"Woś",fullName:"Paweł Woś",slug:"pawel-wos"}]},{id:"35632",title:"Influence of Nozzle Orifice Geometry and Fuel Properties on Flow and Cavitation Characteristics of a Diesel Injector",slug:"influence-of-nozzle-orifice-geometry-and-fuel-properties-on-flow-and-cavitation-characteristics-of-a",signatures:"Sibendu Som, Douglas E. Longman, Anita I. Ramirez and Suresh Aggarwal",authors:[{id:"38903",title:"Dr.",name:"Sibendu",middleName:null,surname:"Som",fullName:"Sibendu Som",slug:"sibendu-som"},{id:"38919",title:"Mr.",name:"Douglas",middleName:null,surname:"Longman",fullName:"Douglas Longman",slug:"douglas-longman"},{id:"38921",title:"Prof.",name:"Suresh",middleName:null,surname:"Aggarwal",fullName:"Suresh Aggarwal",slug:"suresh-aggarwal"},{id:"121046",title:"Ms.",name:"Anita",middleName:null,surname:"Ramirez",fullName:"Anita Ramirez",slug:"anita-ramirez"}]},{id:"35633",title:"Numerical Simulation of Biofuels Injection",slug:"biofuels-injection",signatures:"Jorge Barata and André Silva",authors:[{id:"116411",title:"Dr.",name:"Jorge",middleName:null,surname:"Barata",fullName:"Jorge Barata",slug:"jorge-barata"},{id:"116425",title:"Prof.",name:"André",middleName:null,surname:"Silva",fullName:"André Silva",slug:"andre-silva"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"72280",title:"Photo-Voltaic (PV) Monitoring System, Performance Analysis and Power Prediction Models in Doha, Qatar",doi:"10.5772/intechopen.92632",slug:"photo-voltaic-pv-monitoring-system-performance-analysis-and-power-prediction-models-in-doha-qatar",body:'\n
\n
1. Introduction
\n
Qatar’s rapid development over the past decade led to a remarkable growth on its economy and population. Hence, increasing the demands on food, water, electronics and services. All of which relies on electricity to power the industries such as desalinization plants, farms, commercial infrastructures, semiconductor factories and more. According to the Qatar Water and Electricity Corporation or QWEC, a foremost power generation plant in the country stated that the electricity demand in the country is increasing at an estimated yearly average growth rate of 6–7% in the coming years [1]. In order to address the increasing electricity demand, the state is considering a new energy strategy that would foster sustainability, but also contribute to the reduction of the greenhouse gas emission levels. Fortunately, the gulf region where the country resides, experiences 6 kWh/m2/day amounting to 4449 h/year where 70% comes from sunshine, thus, focusing on optimization of energy extraction from sunlight is a viable solution [2]. In fact, renewable energy sources such as those from photovoltaic cell (PV) plants are estimated to contribute 11% to the global demand by 2050 according to the International Energy Agency (IEA) [3].
\n
Another possible source of renewable energy in Qatar can be harnessed from wind turbines. An assessment on wind energy potential in Qatar conducted by Qatar Petroleum [4] revealed that Qatar may employ use of small and medium wind turbines since 80% of the time wind speed over the country exceeds the critical speed of 3 m/s with annual mean speed over land and offshore of 4.3 and 5.7 m/s, respectively. It was estimated that 150 W/m2 may be harnessed from a 5 m/s wind speed but the power generated from wind turbines may be 8% less compared to the gas fired electricity. The cost projected for an offshore wind turbine is 10% less than the gas-based counterpart. Although wind turbines sound promising as a potential source of renewable energy, it does present several disadvantages compared to PV plants such as: annual maintenance on the turbine’s gear box in contrast to minimal maintenance for the PV, loud noise during operation for nearby inhabitants, and smaller life span of 20–25 years compared to 30 year life span of PV [5]. Qatar does not have immediate plans for installing wind turbines yet, instead it has been focusing on solar energy by allocating US $1 billion investment for the project which includes desalinization plants and a 200 MW power plant by Kahramaa [4]. With the upcoming 2022 FIFA cup, the country aims to be the first carbon neutral world cup utilizing solar energy to power air conditioning and fan zones. Since the state is leaning towards utilizing mostly solar energy to help power its industry, this study was conducted to primarily focus on PV alternative that was designed specifically for Qatar’s environment to test and understand its performance through measurment, prediction and analysis that should provide possible references for its solar industry.
\n
Large-scale PV farms are usually situated where maximum solar energy conversion can be generated which are either semi-arid lands or a desert. However, soaring temperatures reaching 50°C or more, high humidity and heavy sandstorms are some examples of environmental factors that may significantly reduce the efficiency in power generation of the PV systems. These issues are region-specific and may differ from one place to another even within the Gulf region, Hence, it is significant to investigate the modern PV technology under these harsh conditions that are specifically present in Qatar so that performance could be strongly correlated to it [6]. One apparent benefit from this is that the uncertainty of PV performance will be greatly reduced leading to a more predictable and profitable solar megaprojects that are planned to be constructed in the area [7, 8, 9]. The results could also cater to the interests of the manufacturers, researchers and technology enthusiasts in order to develop or innovate solutions.
\n
Efficient energy management is among the benefits from understanding PV performance since some modern communities now use hybrid systems where they integrate renewable sources of energy such as solar PV to determine how it behaves in such systems. In [10], the authors discussed modeling and optimization of urban integrated energy systems to provide an energy plan or policy for a better energy efficiency aiming to mitigate energy crisis experienced in urban communities. In addition, Menetti et al. [11] proposed an efficient energy management that effectively use energy storage systems for renewable energy sources and the electric grid to reduce energy exchanged and power peaks on the grid. The data from the monitoring system becomes a necessary tool for conducting important analysis on the system for a region such as [12] to determine its costs and profit throughout its operation to assess its financial sustenance and feasibility for its possible application to other regions. In addition, it would also aid in contributing to the continuing development of efficient operations in industries to yield better results through exergy and energy analysis such as in [13, 14] and techno-economic analysis in [15, 16]. With increasing amount of studies being conducted centered on renewable energy especially on solar energy and PV, this study will prove useful to the scientific community and may serve as a significant reference to the ones conducted similarly in Qatar.
\n
Several similar investigations in Qatar with same line of inquiries [17, 18, 19, 20, 21, 22, 23, 24, 25, 26] were conducted but none has been able to provide a cost-effective yet reliable system that satisfies the requirement for accessing, monitoring and predicting PV yield. Another major concern is the data acquisition system (DAS); most available commercial DAS tend to be costly when implemented for large solar PV plants. In addition, commercial DAS are inflexible for reconfigurations and modifications for various scenarios, thus, limiting its use. Furthermore, numerous efforts have been conducted in designing and implementing PV monitoring systems that utilize several sensors and data acquisition [27]. The system in [28] included an off-shelf component of Agilent 24902A, wherein the data were transmitted to the wired general purpose instrumentation bus to a computer that is running a LabVIEW™ program to determine the impact of solar irradiance and ambient temperature. Haba [29] developed a designated monitoring system for several PV panels that utilizes three gateways intended for weather station, current and voltage readings and storm detection which were then sent and hosted to online cloud specifically freeboard.io. A readily available commercial DAS was used for investigating the impact of module temperature and solar irradiance on PV efficiency and transmits to a server through the use of GPIB bus and cloud service [30]. Study [31] used a system consisting of LM35 temperature sensor and LDRs (light dependent resistors) for measuring ambient temperature and solar irradiance of PV module, respectively. The data is then transmitted to the computer wirelessly via Wi-Fi by connecting the microcontroller with EGSR7150 modem through its serial interface.
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Forecasting of PV performance were recently introduced to improve the quality of the systems such as providing dispatch management, control operations, power ramp and flicker prediction on hourly basis; and load consumption and production monitoring on daily basis [32]. Parametric models were also utilized for forecasting which are mostly affected by the execution of the component models and factors that are not readily available, thus, affects the accuracy of the system [33]. Recently, ML was introduced to overcome the above drawbacks; which is driven by the interactions between the input and output variables according to the data. Several studies were already conducted like in [34] were they determined the solar potential from rooftops in Switzerland by utilizing ML. Li et al. [35] used ML to predict solar irradiance to precisely determine the PV output utilizing Markov model and regression. Most of these forecasts were conducted on a specific environment, hence it would not be able to provide the same accruacy when used in another locations that exhibits different environmetal parameters like in Doha were it experiences unique intense heat and heavy dust storms that lasts year long. Therefore, we planned to deliberately harness ML for predicting the performance of PV systems from the various environmetal parameters that are present in Doha along the year for viability and bankability of PV energy source.
\n
This study describes the development of an in-house customized DAS system that is viable for monitoring PV systems under Qatar’s climate and which comprises of two parts: hardware and software. Also, the study is enhanced by describing the calibration tools that are necessary in such studies. The remainder of the study is as follows: Section 2 describes the hardware and signal acquisition. Section 3 depicts the ML used for the data gathered throughout the duration of the study. Section 4 discusses the results from the developed system and the ML results. Finally, the conclusion and future work is provided in Section 5.
\n
\n
\n
2. Hardware and signal acquisition
\n
The hardware and signal acquisition system were installed in the Solar Lab facility under the College of Engineering, Qatar University. The ground floor of the solar lab facility houses computer workstation and wireless access point while its rooftop emulates the PV panel remote site where PV panels and data acquisition hardware system are mounted along with all environmental sensors and transducers. Qatar, having an arid environment with extreme ambient temperature easily surpassing 38°C during summer and often approaches 50°C with a humidity of 90% [36].
\n
The authors developed an in-house and customized DAS that acquires six environmental parameters and two electrical parameters enhanced by analog filters with gain and offset adjustments for calibration purposes. The in-house DAS was designed to allow flexibility in order to construct a customized signal conditioning circuit suitable for each sensor that are deemed appropropriate for the range of parameter values in an arid environment. The selected sensors along with the signal conditioning circuit and topology were chosen in order to implement a robust DAS that is appropriate to Doha’s harsh weather condition.
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\nFigure 1 depicts the overall data acquisition framework. Data acquisition starts from the PV panel remote site where the PV panels are installed to ensure maximum exposure to sun’s irradiance, free from shadows due to obstructions. Selection of azimuth and tilt angle of PV panels are also important mounting details that needs to be considered. Two polycrystalline PV panels connected in series were installed in the remote site where the electrical and environmental parameters are needed to be monitored periodically in a specified sequence of steps as shown in the generalized flowchart in Figure 2. Periodic acquisition are normally spaced 15 minutes apart to ensure seamless wireless transmission between the PV panel remote site to the research lab site due to the considering the response time of the hardware. Information collected in the research lab site are stored locally and to the file hosting service of Dropbox™ along with the visualization facility of ThingSpeak™ through and iCloud™ server.
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Figure 1.
Overall data acquisition system.
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Figure 2.
Generalized flowchart of the PV monitoring system.
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A detailed illustration of the connection diagram exhibiting important components of the PV panel remote site is shown in Figure 3. Six environmental and two electrical parameters, namely; (1) ambient temperature, (2) irradiance level, (3) wind speed, (4) surface temperature, (5) relative humidity, (6) dust levels, along with PV voltage and current are carefully studied and chosen by the authors in [37, 38] in order to provide highest probable impact contributing to the correlation to PV panel performance and efficiency, thus, allowing higher reliability when applying ML algorithms in [37, 38, 39]. The system specifications of each sensor are enumerated in Table 1 that includes actual part number of the off-the-shelf sensors along with the manufacturer and range of operation. The details of DAS design and operation were presented by the authors in [37, 38, 39, 40].
\n
Figure 3.
System set-up of the PV panel remote site.
\n
\n
\n
\n
\n
\n\n
\n
Parameter to be measured
\n
Discrete sensor
\n
Manufacturer
\n
Measurement range
\n
\n\n\n
\n
Ambient temperature
\n
LM35
\n
National Instruments
\n
0–70°C
\n
\n
\n
Irradiance
\n
Pyranometer SP-110
\n
Apogee
\n
0 \n\n\nW\n\nm\n2\n\n\n\n to 1500 \n\n\nW\n\nm\n2\n\n\n\n\n
\n
\n
\n
Wind speed
\n
Type 485 Wind sensor
\n
QS-FS
\n
0 \n\n\nm\ns\n\n\n to 32.4 \n\n\nm\ns\n\n\n\n
\n
\n
\n
Surface temperature
\n
Platinum RTD PT100
\n
Farnell
\n
0–100°C
\n
\n
\n
Humidity
\n
HIH-4000-003
\n
Honeywell
\n
0–100%
\n
\n
\n
Dust level
\n
GP2Y1010AU0F Optical Sensor
\n
Sharp
\n
0 \n\n\nmg\n\nm\n3\n\n\n\n to 0.6 \n\n\nmg\n\nm\n3\n\n\n\n\n
\n
\n
\n
Voltage
\n
Voltage transducer LV 25-P
\n
LEM
\n
0 V to 40 V
\n
\n
\n
Current
\n
Hall effect current transducer LA 100-P
\n
LEM
\n
0A to 5A
\n
\n\n
Table 1.
PV system specifications of each sensor.
\n
\nFigure 4 exhibits the simplified connection of various elements to process the required signal for redundant storage and visualization in the research lab set-up. The computer workstation uses LabVIEW™ program to process data that allows visualization of recently acquired data as depicted in Figure 5.
\n
Figure 4.
System set-up of the research lab site.
\n
Figure 5.
Sample visualization of collected data using LabVIEW™ in the computer workstation.
\n
\n
\n
3. Power prediction using machine learning
\n
ML is the process of training a system to automatically predict output from given inputs. The system is trained using available set of inputs and their respective outputs. The concept of ML is useful in biomedical applications [41, 42], power prediction [43] and in general for any data processing and analysis studies. ML will be used to learn from the large amount of monitoring data collected from the setup discussed in the previous section and this phase is the training phase. During the training phase a part of the input data used for training is kept for validation purposes of the trained network. The validation accuracy is a metric used to determine how good or bad a trained ML network is. This ML trained network is then used for testing some data, which was unknown to the ML network, and is used to check if the ML trained network can actually predict the output correctly. The best performing ML network can later be used to predict the PV performance in the future based on the environmental and electrical inputs. The various stages that are involved in the ML are shown in Figure 6 and will also be discussed in details in the sub sections below.
\n
Figure 6.
Stages involved in ML training and testing phase.
\n
\n
3.1 Pre processing
\n
It is always important to make sure that the data given to the ML network for training is correctly formatted, making sure all outliers in the data or data which are incorrect and not trustable are removed. The data should be made in a format which is acceptable to the ML network in whichever platform it is being operated on. The ML Toolbox in Matlab 2019a version was used in the study. There are many other popular ML platforms available such as TensorFlow, Keras, Shogun, and RapidMiner.
\n
\n
\n
3.2 Feature selection
\n
Once the data (input and output) for the training and testing purpose is ready, it is important to select the inputs that can help in predicting the output better. Sometimes giving more input or options to help in prediction can lead to overfitting problem. Overfitting is an issue where a ML network is trained to work the best for only the trained dataset and predicts mostly wrong outputs in the testing phase. This process of selecting the input data that can increase the testing accuracy is called feature selection. Selection of features is the process of selecting a subset of relevant, high-quality and non-redundant features to create learning models with better accuracy [44, 45]. Well known feature selection techniques – Correlation feature selection (CFS) and Relief feature selection (ReliefF) was used in this study. CFS technique selects feature sub-sets based on correlation-based heuristic evaluation function and ReliefF is an instance-based algorithm that assigns a relevance weight to each feature that reflects its ability to differentiate class values [43].
\n
\n
\n
3.3 Prediction models
\n
Once the data that will be given as input to the ML training phase is selected, then there are several ML techniques that can be used to see which techniques help in reaching better performance. The techniques used in this study can be broadly classified into two categories: Classical ML Technique and Artificial Neural Network. These techniques are compared in the performance in prediction during the testing phase and the best performing technique is archived for future use.
\n
\n
\n
3.4 Classical machine learning
\n
Several simple and popular regression and prediction models are stated in this work to estimate the PV output power. These are namely Simple Linear Regression [46], Gaussian Process Regression (GPR) [47] from the regression learner, and M5P regression tree [37, 48]. Simple linear regression model has a linear relationship between the output response and the input parameters. GPR involves a Gaussian process using lazy learning and a measure of the point similarity (kernel function) to predict the value from the training data for an unseen point. The M5P regression tree uses algorithm which contains if and else statements [48, 49] . In other words, predicted power will be the result of “if… then…else…” statements.
\n
\n
\n
3.5 Artificial neural network
\n
Artificial Neural Network (ANN) (Figure 7) can be thought of a replication of how the human nervous system works, but as it is artificial thus it gets its name [50]. ANN has three major layers: (1) Input Layer, Output Layer and the Hidden Layer. The input layer are the artificial neurons where the actual learning happens and is also the layer where the input is fed. Each neuron in this layer has specific weights, which are details used to solve a specific problem. These weighted summed inputs are used in the hidden layers or in the transfer functions. Transfer functions are then inputs to activation function which tries to predict the output or provides the error back to the network as a feedback. This feedback acts as learning for the input layers again to try providing inputs to the activation function to help in better prediction.
\n
Figure 7.
ANN architecture and its main components.
\n
There are several Training Algorithms (TA) available in the Matlab implementation of ANN and each of them have their advantages and disadvantages and each application can have a specific TA giving better results than the others due to the nature of the data. It is always important to explore various combinations of number of hidden layers and training functions to find the best combination that predicts the PV power most accurately, as shown in Figure 8. The algorithm first varies the training algorithms, then the number of hidden layers and then does many tries using the combination. During each trial the algorithm stores the network with best performance for testing purpose. The final best network is used for predicting the PV power using the input variables.
\n
Figure 8.
Method to find the best ANN to predict PV power.
\n
\nFigure 9 summarizes the network settings for the ANN based PV power prediction. The optimum number of hidden layers providing the best model were different for all features (60), CFS technique (260) and ReliefF technique (180) and were found using the algorithm stated in Figure 8.
\n
Figure 9.
Details of the ANN.
\n
In order to compare between the various categories, techniques of ML and also the various feature selection techniques the below statistical parameters were used as performance metrics [51].
\n\n\nwhere\n\nX\n\nis the actual data vector\n,\nY\n\nand\n\n\nY\n¯\n\n\nare\n\nthe predicted data vector and mean of the predicted data vector\n\n.
\n
\n
\n
\n
4. Results from the setup and machine learning
\n
The prototype system (setup shown in Figures 1 and 3) was used for collecting the PV and environmental parameters and PV power output data from the period November 2014 until October 2016. Summary of the PV and environmental parameters and the data used for deriving the predictive model of the PV power is shown in Table 2.
\n
Table 2.
Details of the environment parameters used for the predictive model.
\n
Table 3.
Selected features vector.
\n
\nTable 3 summarizes the parameters selected based on the feature selection techniques CFS and relief F.
\n
\nTable 4 summarizes the performance of the different classical ML techniques with the different feature selection techniques. It shows both the Training and Testing Phase performance metrics. It can be clearly seen the best performance is the CFS feature selection technique using the GPR algorithm with RMSE of 12.7144 watts compared to the maximum power of 114.2017 watts generated from the setup, as shown in Table 2.
\n
Table 4.
Performance comparison between the various regression techniques.
\n
\nTable 5 summarizes the performance of the ANN best trained network found using the algorithm in Figure 8 and with the different feature selection techniques. It can be clearly seen that the ANN trained network outperforms the techniques in the classical ML techniques. In ANN, without feature selection techniques provides the best testing performance with RMSE of 5.48 watts compared to the maximum power of 114.20 watts generated from the setup, as shown in Table 2.
\n
Table 5.
Performance comparison between the various ANN techniques.
\n
\n
\n
5. Conclusion and future work
\n
A customized PV system was developed at Qatar University to monitor, analyze and evaluate the performance of PV using various weather factors. The study also showed details of how the data collected could be used for training different ML algorithms which were compared using different statistical analytical tools. Several feature selection techniques were also used to avoid the problem of overfitting. Comparison between the different ML techniques and different feature selection techniques helped in concluding an ANN model to be used for predicting PV performance using different environment and electrical parameters. The paper also showed the opportunity of tuning the ANN by varying the number of hidden layers and changing the training algorithm. This study describes the development of an in-house customized DAS system that is viable for monitoring PV systems under Qatar’s climate and which comprises of two parts: hardware and software. Also, the study is enhanced by describing the calibration tools that are necessary in such studies. The remainder of the study is as follows: Section 2 describes the hardware and signal acquisition. Section 3 depicts the ML used for the data gathered throughout the duration of the study. Section 4 discusses the results from the developed system and the ML results. Finally, the conclusion and future work is provided in Section 5.
\n
\n
Acknowledgments
\n
The authors would like to thank Qatar University for the financial, technical, and administrative support, without which this work would have not been achieved.
\n
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"PV, environmental parameters, sensors, data acquisition system, iCloud storage, PV power prediction, ML",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72280.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72280.xml",downloadPdfUrl:"/chapter/pdf-download/72280",previewPdfUrl:"/chapter/pdf-preview/72280",totalDownloads:134,totalViews:0,totalCrossrefCites:0,dateSubmitted:"September 15th 2019",dateReviewed:"April 22nd 2020",datePrePublished:"May 22nd 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"This study aims developing customized novel data acquisition for photovoltaic systems under extreme climates by utilizing off-the-shelf components and enhanced with data analytics for performance evaluation and prediction. Microcontrollers and sensors are used to measure meteorological and electrical parameters. Customized signal conditioning, which can withstand high-temperature along with microcontrollers’ development boards enhanced with appropriate interfacing shields and wireless data transmission to iCloud IoT platforms, is developed. In addition, an automatically controllable in-house electronic load of the PV system was developed to measure the maximum power possible from the system. LabVIEW™ program was used to allow ubiquitous access and processing of the recorded data over the used IoT. Furthermore, machine learning algorithms are utilized to predict the PV output power by utilizing data collected over a two-year span. The result of this study is the commissioning of original hardware for PV study under extreme climates. This study also shows how the use of specific ML algorithms such as Artificial Neural Network (ANN) can successfully provide accurate predictions with low root-mean-squared error (RMSE) between the predicted and actual power. The results support reliable integration of PV systems into smart-grids for efficient energy planning and management, especially for arid and semi-arid regions.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72280",risUrl:"/chapter/ris/72280",signatures:"Farid Touati, Amith Khandakar, Muhammad E.H. Chowdhury, Antonio Jr. S. P. Gonzales, Christian Kim Sorino and Kamel Benhmed",book:{id:"9385",title:"Renewable Energy",subtitle:null,fullTitle:"Renewable Energy",slug:null,publishedDate:null,bookSignature:"Associate Prof. Tolga Taner",coverURL:"https://cdn.intechopen.com/books/images_new/9385.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"197240",title:"Associate Prof.",name:"Tolga",middleName:null,surname:"Taner",slug:"tolga-taner",fullName:"Tolga Taner"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Hardware and signal acquisition",level:"1"},{id:"sec_3",title:"3. Power prediction using machine learning",level:"1"},{id:"sec_3_2",title:"3.1 Pre processing",level:"2"},{id:"sec_4_2",title:"3.2 Feature selection",level:"2"},{id:"sec_5_2",title:"3.3 Prediction models",level:"2"},{id:"sec_6_2",title:"3.4 Classical machine learning",level:"2"},{id:"sec_7_2",title:"3.5 Artificial neural network",level:"2"},{id:"sec_9",title:"4. Results from the setup and machine learning",level:"1"},{id:"sec_10",title:"5. Conclusion and future work",level:"1"},{id:"sec_11",title:"Acknowledgments",level:"1"},{id:"sec_14",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nQatar Electricity and Water Corporation. Qatar’s Installed Power Capacity Doubles in 2 Years as Demand Rises [Internet]. 2013. Available from: https://www.qewc.com/qewc/en/index.php/qewc/77-gulf-times/125-qatar-s-installed-power-capacity-doubles-in-2-years-as-demand-rises [Accessed: 19 December 2019]\n'},{id:"B2",body:'\nAksakal A, Rehman S. Global solar radiation in northeastern Saudi Arabia. Renewable Energy. 1999;17:461-472. DOI: 10.1016/S0960-1481(98)00769-1\n'},{id:"B3",body:'\nUS Energy Information Administration. International Energy Outlook [Internet]. 2018. Available from: http://www.eia.gov/forecasts/ieo/pdf/0484(2013).pdf [Accessed: 20 October 2019]\n'},{id:"B4",body:'\nAssessment of solar and wind energy potential in Qatar [Internet]. 2013. Available from: http://www.jccp.or.jp/international/conference/docs/14assessment-of-solar and-wind-energy-potential-in.pdf [Accessed: 18 April 2020]\n'},{id:"B5",body:'\nRenewables comparison: wind vs. solar energy [Internet]. 2016. Available from: https://icap.sustainability.illinois.edu/files/projectupdate/4045/wind%20v%20solar.pdf [Accessed: 18 April 2020]\n'},{id:"B6",body:'\nTouati FA, Hitmi MAA, Bouchech HJ. Study of the effects of dust, relative humidity, and temperature on solar pv performance in Doha: Comparison between monocrystalline and amorphous PVS. International Journal of Green Energy. 2013;10:680-689. DOI: 10.1080/15435075.2012.692134\n'},{id:"B7",body:'\nAdnene C, Hamadi Z, Moncef J. Control and real time monitoring of less battery storage photovoltaic plants. International Journal of Sustainable Energy. 2005;24:87-98. DOI: 10.1080/14786450512331329574\n'},{id:"B8",body:'\nTina GM, Grasso AD. Remote monitoring system for stand-alone photovoltaic power plants: The case study of a PV-powered outdoor refrigerator. Energy Conversion and Management. 2014;78:862-871. DOI: 10.1016/j.enconman.2013.08.065\n'},{id:"B9",body:'\nFathi AE, Nkhaili L, Bennouna A, Outzourhit A. Performance parameters of a standalone PV plant. Energy Conversion and Management. 2014;86:490-495. DOI: 10.1016/j.enconman.2014.05.045\n'},{id:"B10",body:'\nLi S, Guo L, Zhang P, Wang H, Cai Z, Zhu X, et al. Modeling and optimization on energy efficiency of urban integrated energy system. In: 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2); 20–22 October 2018. Beijing, China: IEEE; 2018. pp. 1-6\n'},{id:"B11",body:'\nMenniti D, Pinnarelli A, Sorrentino N, Vizza P, Burgio A, Brusco G, et al. A real-life application of an efficient energy management method for a local energy system in presence of energy storage systems. In: IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe); 12–15 June 2018. Palermo, Italy: IEEE; 2018. pp. 1-6\n'},{id:"B12",body:'\nSwarnkar NM, Gidwani L. Economic and financial assessment of integrated solar and wind energy system in Rajasthan, India. In: International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC); 22–23 March 2017. Melmaruvathur, India: IEEE; 2017. pp. 471-476\n'},{id:"B13",body:'\nKumar M, Saini R. Energy and exergy analysis for heliostat based solar thermal power plant. In: 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES); 22–24 October 2018. Delhi, India: IEEE; 2018. pp. 120-126\n'},{id:"B14",body:'\nHamdi I, Kooli S. Exergy and energy analysis of the solar drying processes of tomatoes in Tunisia. In: 9th International Renewable Energy Congress (IREC); 20–22 March 2018. Hammamet, Tunisia: IEEE; 2018. pp. 1-6\n'},{id:"B15",body:'\nŠimunović J, Barbir F, Radica G, Klarin B. Techno-economic analysis of PV/wind turbine stand-alone energy system. In: 4th International Conference on Smart and Sustainable Technologies (SpliTech); 18–21 June 2019. Split, Croatia: IEEE; 2019. pp. 1-5\n'},{id:"B16",body:'\nBenmedjahed M, Maouedj R. Technical and economic analysis of wind turbine system for isolated location at Adrar in Algeria. In: 6th International Renewable and Sustainable Energy Conference (IRSEC); 5–8 December 2018. Rabat, Morocco: IEEE; 2018. pp. 1-4\n'},{id:"B17",body:'\nJeevandoss CR, Kumaravel M, Kumar VJ. Sunlight based I-V characterization of solar PV cells. In: Instrumentation and Measurement Technology Conference; 10–12 May 2011. Binjiang, China: IEEE; 2011. pp. 1-4\n'},{id:"B18",body:'\nAli MA, Emziane M. Performance analysis of rooftop PV systems in Abu Dhabi. Energy Procedia. 2013;42:89-697. DOI: 10.1016/j.egypro.2013.11.071\n'},{id:"B19",body:'\nAtmaja TD. Façade and rooftop PV installation strategy for building integrated photo voltaic application. Energy Procedia. 2013;32:105-114. DOI: 10.1016/j.egypro.2013.05.014\n'},{id:"B20",body:'\nCongedo PM, Malvoni M, Mele M, Giorgi MGD. Performance measurements of monocrystalline silicon PV modules in South-Eastern Italy. Energy Conversion and Management. 2013;68:1-10. DOI: 10.1016/j.enconman.2012.12.017\n'},{id:"B21",body:'\nDubey S, Sarvaiya JN, Seshadri B. Temperature dependent photovoltaic (PV) efficiency and its effect on PV production in the world - A review. Energy Procedia. 2013;33:311-321. DOI: 10.1016/j.egypro.2013.05.072\n'},{id:"B22",body:'\nTebibel H, Labed S. Performance results and analysis of self-regulated PV system in Algerian Sahara. Renewable Energy. 2013;60:691-700. DOI: 10.1016/j.renene.2013.06.032\n'},{id:"B23",body:'\nAste N, Pero CD, Leonforte F. PV technologies performance comparison in temperate climates. Solar Energy. 2014;109:1-10. DOI: 10.1016/j.solener.2014.08.015\n'},{id:"B24",body:'\nD’Orazio M, Perna CD, Giuseppe ED. Experimental operating cell temperature assessment of BIPV with different installation configurations on roofs under Mediterranean climate. Renewable Energy. 2014;68:378-396. DOI: 10.1016/j.renene.2014.02.009\n'},{id:"B25",body:'\nSaber Esmail M, Lee SE, Manthapuri S, Yi W, Deb C. PV (photovoltaics) performance evaluation and simulation-based energy yield prediction for tropical buildings. Energy. 2014;71:588-595. DOI: 10.1016/j.energy.2014.04.115\n'},{id:"B26",body:'\nMaturi L, Belluardo G, Moser D, Buono MD. BiPV system performance and efficiency drops: Overview on PV module temperature conditions of different module types. Energy Procedia. 2014;48:1311-1319. DOI: 10.1016/j.egypro.2014.02.148\n'},{id:"B27",body:'\nAmhani A, Attia HA. Online multi-parameters electronic monitoring system for solar photovoltaic panel applications. In: International Conference on Electrical and Computing Technologies and Applications (ICECTA); 21–23 November 2017. Ras Al Khaimah, United Arab Emirates: IEEE; 2018. pp. 1-4\n'},{id:"B28",body:'\nChouder A, Silvestre S, Taghezouit B, Karatepe E. Monitoring, modelling and simulation of PV systems using LabVIEW. Solar Energy. 2013;91:337-349. DOI: 10.1016/j.solener.2012.09.016\n'},{id:"B29",body:'\nHaba CG. Monitoring photovoltaic parks for damage prevention and optimal operation. In: International Conference on Electromechanical and Power Systems (SIELMEN); 11–13 October 2017. Iasi, Romania: IEEE; 2017. pp. 321-326\n'},{id:"B30",body:'\nTorres M, Muñoz FJ, Muñoz JV, Rus C. Online monitoring system for stand-alone photovoltaic applications—Analysis of system performance from monitored data. Journal of Solar Energy Engineering. 2012;134:1-9. DOI: 10.1115/1.4005448\n'},{id:"B31",body:'\nAnwari M, Hidayat A, Hamid MI. Wireless data acquisition for photovoltaic power system. In: INTELEC 2009 - 31st International Telecommunications Energy Conference; 18–22 October 2009. Incheon, South Korea: IEEE; 2009. pp. 1-4\n'},{id:"B32",body:'\nTheocharides S, Makrides G, Georghiou G, Kyprianou A. Machine learning algorithms for photovoltaic system power output prediction. In: IEEE International Energy Conference (ENERGYCON); 3–7 June 2018. Limassol, Cyprus: IEEE; 2018. pp. 1-6\n'},{id:"B33",body:'\nLorenz E, Scheidsteger T, Hurka J, Heinemann D, Kurz C. Regional PV power prediction for improved grid integration. Progress in Photovoltaics: Research and Applications. 2011;19:757-771. DOI: 10.1002/pip.1033\n'},{id:"B34",body:'\nAssouline D, Mohajeri N, Scatterzzini J. Quantifying rooftop photovoltaic solar energy potential: A machine learning approach. Solar Energy. 2016;141:278-296. DOI: 10.1016/j.solener.2016.11.045\n'},{id:"B35",body:'\nLi J, Ward J, Tong J, Collins L, Platt G. Machine learning for solar irradiance forecasting of photovoltaic system. Renewable Energy. 2016;90:542-553. DOI: 10.1016/j.renene.2015.12.069\n'},{id:"B36",body:'\nAverage Weather in Doha, Qatar, Year Round - Weather Spark [Internet]. 2019. Available from: https://weatherspark.com/y/105083/Average-Weather-in-Doha-Qatar-Year-Round [Accessed: 01 November 2019]\n'},{id:"B37",body:'\nTouati F, Chowdhury NA, Benhmed K, Gonzales AS, Al-Hitmi MA, Benammar M, et al. Long-term performance analysis and power prediction of PV technology in the State of Qatar. Renewable Energy. 2017;113:952-965. DOI: 10.1016/j.renene.2017.06.078\n'},{id:"B38",body:'\nTouati F, Al-Hitmi MA, Chowdhury NA, Hamad JA, Gonzales AS. Investigation of solar PV performance under Doha weather using a customized measurement and monitoring system. Renewable Energy. 2016;89:564-577. DOI: 10.1016/j.renene.2015.12.046\n'},{id:"B39",body:'\nBenhmed K, Touati F, Al-Hitmi M, Chowdhury NA, Gonzales AS, Qiblawey Y, et al. PV power prediction in Qatar based on machine learning approach. In: IRSEC 2018 - 6th International Renewable and Sustainable Energy Conference (IRSEC); 05–08 December 2018. Rabat, Morocco: IEEE; 2018. pp. 174-177\n'},{id:"B40",body:'\nTouati F, Gonzales AS, Qiblawey Y, Benhmed K. A customized PV performance monitoring system in Qatar’s harsh environment. In: IRSEC 2018 - 6th International Renewable and Sustainable Energy Conference (IRSEC); 05–08 December 2018. Rabat, Morocco: IEEE; 2018. pp. 134-139\n'},{id:"B41",body:'\nChowdhury ME, Alzoubi K, Khandakar A, Khallifa R, Abouhasera R, Koubaa S, et al. Wearable real-time heart attack detection and warning system to reduce road accidents. Sensors. 2019;19:2780. DOI: 10.3390/s19122780\n'},{id:"B42",body:'\nChowdhury ME, Khandakar A, Alzoubi K, Mansoor S, Tahir AM, Reaz MBI, et al. Real-time smart-digital stethoscope system for heart diseases monitoring. Sensors. 2019;19:2781. DOI: 10.3390/s19122781\n'},{id:"B43",body:'\nKhandakar A, Chowdhury MEH, Khoda Kazi M, Benhmed K, Touati F, Al-Hitmi M, et al. Machine learning based photovoltaics (PV) power prediction using different environmental parameters of Qatar. Energies. 2019;12:2782. DOI: 10.3390/en12142782\n'},{id:"B44",body:'\nGuyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research. 2003;3:1157-1182. DOI: 10.1162/153244303322753616\n'},{id:"B45",body:'\nWang H, Khoshgoftaar TM, Gao K, Seliya N. High-dimensional software engineering data and feature selection. In: 21st IEEE International Conference on Tools with Artificial Intelligence; 2–4 November 2009. Newark, NJ, USA: IEEE; 2009. pp. 83-90\n'},{id:"B46",body:'\nYan X, Su X. Linear regression analysis: Theory and computing. World Scientific. 2009;1:9-28. DOI: 10.1142/6986\n'},{id:"B47",body:'\nMacKay DJ, Mac Kay DJ. Information Theory, Inference and Learning Algorithms. Cambridge, United Kingdom: Cambridge University Press; 2003. DOI: 10.1017/S026357470426043X\n'},{id:"B48",body:'\nQuinlan JR. Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence. 1992. DOI: 10.1142/9789814536271\n'},{id:"B49",body:'\nWang Y, Witten IH. Induction of Model Trees for Predicting Continuous Classes. Hamilton, New Zealand: Department of Computer Science, University of Waikato; 1996\n'},{id:"B50",body:'\nMellit A, Pavan AM. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy. 2010;84:807-821. DOI: 10.1016/j.solener.2010.02.006\n'},{id:"B51",body:'\nSheiner LB, Beal SL. Some suggestions for measuring predictive performance. Journal of Pharmacokinetics and Biopharmaceutics. 1981;9:503-512. DOI: 10.1007/BF01060893\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Farid Touati",address:null,affiliation:'
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"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
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Carlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
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Open Access background
\\n\\n
The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
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IntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\n
At IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n
“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
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Open Access Standards followed by IntechOpen
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OAI-PMH
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As a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
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License
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Book chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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Peer Review Policies
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All scientific works are Peer Reviewed prior to publishing. Read more
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OA Publishing Fees
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The Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
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Digital Archiving Policy
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IntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\n
IntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\n
At IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n
“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\n
Open Access Standards followed by IntechOpen
\n\n
OAI-PMH
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As a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
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License
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Book chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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Peer Review Policies
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All scientific works are Peer Reviewed prior to publishing. Read more
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OA Publishing Fees
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The Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
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Digital Archiving Policy
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IntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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