Presents wind power software models prediction internationally.
\r\n\tThere are a variety of approaches to reversing biodiversity loss, ranging from economic, to ecological and ethical. The utilitarian approach to conservation, bolstered by the concept of ecosystem services, can be utilized to improve the conservation case by supplementing the burgeoning biodiversity rhetoric. To address this issue, a pluralistic approach to biodiversity is required for conservation and sustainability.
",isbn:"978-1-80356-339-8",printIsbn:"978-1-80356-338-1",pdfIsbn:"978-1-80356-340-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"ab014f8ed1669757335225786833e9a9",bookSignature:"Dr. Gopal Shukla, Dr. Jahangeer Bhat and Dr. Sumit Chakravarty",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11460.jpg",keywords:"Ecosystem Services, Intrinsic Value, Global Trends in Biodiversity Loss, Convention on Biological Diversity, Utilitarian Value, Biodiversity Conservation, Perception, In Situ and Ex Situ Conservation, Nature Conservation, Sustainable Development Goals, Drivers of Degradation, Prioritizing Biodiversity",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 17th 2022",dateEndSecondStepPublish:"April 22nd 2022",dateEndThirdStepPublish:"June 21st 2022",dateEndFourthStepPublish:"September 9th 2022",dateEndFifthStepPublish:"November 8th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Gopal Shukla, prior to becoming an assistant professor, has worked under NAIP (National Agricultural Innovation Project), NICRA ( National Innovations on Climate Resilient Agriculture), and SERB (Science and Engineering Research Board) projects. The focus of his research and development work is forest conservation. He has authored 75 research papers, 10 book chapters and has edited 5 books.",coeditorOneBiosketch:"Dr. Jahangeer is a Guest Associate Editor in Frontiers in the Environmental Science journal and is the first researcher to report the first time growing of Acacia dealbata Link. (Silver Wattle), an invasive species in the high altitudes of the Himalayas. He has 11 years of research and 8 years of teaching experience with a publication record of more than 60, including research articles, review papers, conference papers, and books of national and international repute.",coeditorTwoBiosketch:"Dr. Chakravarty, Ph. D., has a wide experience in forestry training, research, and development. He is currently working as a Professor in Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India. He has conducted research on several aspects of forestry, agroforestry, medicinal plants, and climate change. 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He has been instrumental in developing HE and TVET streams of forestry and allied programmes and worked closely in accreditation with the Fiji Higher Education Commission and forestry stakeholders. Before joining Fiji National University, he worked for HNB Garhwal University, Srinagar, India, and has 11 years of research and 8 years of teaching experience with a publication record of more than 60, including research articles, review papers, conference papers, and books of national and international repute. Dr. Jahangeer reviews research articles for several scientific journals and has handled research projects in his capacity as Principal Investigator and Co-Principal Investigator. His major interests lie in emerging issues in forestry including conservation of biodiversity, traditional knowledge of plants, and sustainable management of forest resources. His focus of research is vegetation ecology, ethnobotany, and evaluation of ecosystem services, forest plant biodiversity, climate change, and socio-cultural issues in forestry. Dr. Jahangeer is currently working at the College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.",institutionString:"Central Agricultural University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Central Agricultural University",institutionURL:null,country:{name:"India"}}},coeditorTwo:{id:"94999",title:"Dr.",name:"Sumit",middleName:null,surname:"Chakravarty",slug:"sumit-chakravarty",fullName:"Sumit Chakravarty",profilePictureURL:"https://mts.intechopen.com/storage/users/94999/images/system/94999.jpg",biography:"Dr. Sumit Chakravarty, Ph.D., has wide experience in forestry training, research, and development. 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Since stakeholders have different standing positions for certification, there is a reflection that productivity cannot be improved by mutually balancing each other. Safety2.0 focuses on the realization of “cooperative safety” that creates a highly safe condition by mutually exchanging information among the elements that constitute systems based on IoT. Therefore, under Safety2.0, stakeholders are required to share their wisdom with each other and to manufacture products based on IoT. What is the meaning of “relying on IoT” for railways? Radio train control systems advanced train administration and communications system (ATACS) and communications-based train control (CBTC) will be analyzed and evaluated from the viewpoint of Safety2.0, and future train control will be considered.
\nThe new safety concept named Safety2.0 is not unrelated to the desire to overcome occupational safety occlusions at production sites. In the summer of 2015, the Safety2.0 Preparatory Committee (renamed the “Safety2.0 Promotion Committee” in 2016) was established. The conclusion was that the essential elements of the system exchanged information with each other to create optimal safety, which was the construction of a cooperative safety methodology suitable for the IoT era [1, 2]. Safety2.0 was, of course, preceded by Safety0.0 and Safety1.0, which supported present-day safety. However, the use of IoT is very effective in overcoming the sense of occlusion on it. In this chapter, we review the changes in safety initiatives from the perspective of Safety0.0 and Safety1.0, and confirm the today’s status of Safety2.0. In addition, practical activities aimed at realizing Safety2.0 have begun, and we would like to introduce the situation.
\nDirection calls are famous for railway safety culture. This is also an easy-to-understand case of Safety0.0, which attempts to prevent accidents by drawing attention and keeping the spirit awake at all times. Japan has been regarded as a leader in the sustainable implementation of the “Zero-Accident Motion” and other activities leading to Safety0.0. Looking back at this, various activities have been carried out, including the enactment of the Occupational Safety and Health Law in 1972 and the start of the “Everyone’s Participation in Zero Accidents” motion by the Center Industrial Accident Prevention Association in 1973. On the other hand, it is interesting to note that the European version of the Zero-Disaster Motion, which is a top-down movement but corresponds to Safety0.0, is beginning to take place in Europe, where the International Society for Social Security (ISSA; International Social Security Association) has achieved outcomes at Safety1.0 through the launch of Vision Zero and the launch of Zero Accidents Forum by Europe and Finland.
\nThere are limitations to ensuring safety by Safety0.0 alone, since no mistakes will be made and the machinery will be destroyed, no matter how well people are trained. Therefore, technological efforts to implement some sort of safety measures for “goods” such as machinery and systems have progressed on the assumption that “people make mistakes” and “machinery breaks down.” In Europe, this was established as a mandatory standard, and work accidents were greatly reduced by providing industrial machinery with safety protection measures. The basic idea was to establish a barrier between industrial machinery, which is a hazardous source, and humans, and to establish a mechanism for moving machinery only when humans are absent. This is Safety1.0. In Japan, the Industrial Safety and Health Law was revised in 2005, and risk assessment was added as an obligation to make initiatives. Efforts learned from European experiences and outcomes are now being developed. For this implementation, a number of safety mechanisms have been developed and incorporated into devices at each industrial site. However, even though it is isolated from hazardous sources, it is not possible to completely isolate workers during maintenance. In addition, many sites are difficult to isolate, such as construction work.
\nSafety2.0 is a system that ensures methodology suitable for the IoT era, in which the essential elements constituting the system exchange information with each other to achieve optimal safety [1, 2]. In this respect, it is different from the idea of relying on human attention (Safety0.0) and of taking some protective measures against human errors and mechanical failures to ensure safety (Safety1.0). These relationships are summarized in Figure 1. In Safety0.0, risks exist in a wide range, including coexistence areas, in order to prevent accidents due to attention and judgment. On the other hand, in Safety1.0, the risks were reduced by dividing the human area into the machinery domain and by providing various safety measures in the machine domain. Safety of isolation that creates as little coexistence as possible is fundamental.
\nComparison among Safety(x.x).
On the other hand, in Safety2.0, machineries and humans exchange information and cooperate with each other to ensure safety, so that both machines and humans can coexist with each other. In addition, since appropriate safety using information is maintained during operation, the overall risk is greatly reduced.
\nThe Nikkei BP brochure [1] explains Safety2.0 as follows: “Frankly speaking Safety2.0 is a collaborative safety built by people, goods, and the environmental in cooperation with each other. The best mean to achieve this Safety2.0 is to make rapid progress today. In Safety1.0, there was only a choice between “stop” and “go.” In Safety2.0, however, detailed operations are carried out by exchanging information between people and machineries, and the safely coexistence between both is aimed at (Omitted hereafter.).” A case of Safety2.0 is indicated in Figures 2 and 3.
\nAn example of Safety2.0 (Case 1).
An example of Safety2.0 (Case 2).
Management understandings and support are essential to Safety2.0’s in-house promotion. Fortunately, IoT-based safety technologies were widely developed prior to the conceptual development of Safety2.0, and there were certain grounds to accept Safety2.0. However, in order to expand Safety2.0 from Japan to a wide range of countries as well as in Japan, we would like to have a promotion base. To this end, the Safety Global Promotion Mechanism (IGSAP; The Institute of Global Safety Promotion) was established.
\nIGSAP has established the safety management forum as a forum for managers and managers to gather and replace information and experiences in order to actively engage in the safety of customers, employees, and the safety of the company as a company. In addition, Japan has been vigorously inviting European opinion leaders to work to eradicate occupational accidents under the Vision Zero and replace opinions through visits to Europe. In February 2018, NIPPO’s automatic stop tire roller/wheel loader, equipped with an emergency stop technology for construction machinery, was the first Safety2.0 automatic stop tire roller/wheel loader to be registered.
\nOn October 14, 1872, Japan’s railway opened between Shimbashi and Yokohama. Following the opening of the railway, the railway construction regulations and railway dormitory train transport regulations were enacted, and other rules for ensuring safety operation were rapidly established. This is a summary of the standards and regulations that form the base of Safety0.0. Technologies have also been imported overseas. For example, 1887, a voucher-type blocking method with the use of occlusion telegraphs was established between Kyoto and Osaka, and a second-class mechanical interlocking device was installed at the junction of the Yamanote Line and the Tokaido Line in Shinagawa Station. In addition, the manufacture of railway signals and interlocking equipment began at the Mimura Factory of Tokyo Tsukishima, and the move toward in-house production also progressed [3].
\nSignaling devices based on these technologies are mainly designed to ensure routes and link signals, and the safety of drivers can be guaranteed if they operate according to the signals. However, there was an accident caused by a mistake by the station manager that the train line between stations was forgotten and the opposite route was set in the single-line section. The emphasis was placed on raising human attention in the meaning that mistakes could lead to accidents as they were.
\nMachinery is a hazard source in the world of machine safety, such as factories. For this reason, a mechanism for confirming that no human or body part exists in the work area of the machinery and allowing the machine to operate only at that time has been adopted. Furthermore, the sensors used therein and the devices for confirming safety are fail-safe, and the Safety1.0 mode is constructed.
\nOn the other hand, trains are the main source of hazard in railways. For this reason, the “concept of isolation” that allows only one train to exist in one section was established as a blockage. In addition, in the station premises, an interlocking device has been developed, which ensures absolute safety even if erroneous signal handling is performed. In addition, the use of fail-safe orbit circuits has resulted in advanced signal systems. However, even under these mechanisms, mistakes of the driver, such as signal advancement, can cause an accident. For this reason, in-vehicle alarms have been developed, which give an alarm to wake up when a stop signal is approached, and they have evolved to an ATS, which applies an emergency brake when a driver does not perform a predetermined treatment.
\nHowever, the technology introduced in the field of railways and industrial machinery that is safe but that is basically safe to stop was not the same as the technology introduced in the case where flight continuity is safe instead of stopping as in the case of an aircraft. In addition, own technologies were developed in the industrial machinery field and railways. As a result, no agreement was reached on common safety and fail-safe technologies across industries, and there was no common measure of safety.
\nIn the 1980s, computers were introduced into Safety1.0, which had been secured with sophisticated circuitry. As a result, the aspects of safety technologies that have been uniquely pursued in each industry have changed. In addition to the conventional technology, the concern of engineers in various industries has been to ensure the safety of the computer itself (hardware) used and to prevent bugs (quality assurance) in the software to be incorporated.
\nComputer hardware safety has been solved by redundant configuration and verification of processing results and appropriate integration of diagnostic circuitry, but the methodology has been discussed across industries. Software has a common issue: how to develop high-quality, bug-free software. What is important is that the sophisticated circuits that have once been inherited as the essence of safety technology by various industry sectors have all been incorporated as software logic and have not appeared on the surface.
\nThis resulted in a deeper recognition of common methodologies across industrials and the establishment of a new IEC61508 of international standards that can be applied across industries under the new concept of “functional safety.” In order to ensure safety in the age of functional safety, first of all, the safety level of the target systems is determined as safety integrity levels (SILs) as a result of the risk analysis. In addition, the design requirements of the hardware and the targets of the hazardous side failure rate are indicated in accordance with the SIL value. In software, the design requirements for each phase of the life cycle are determined according to the SIL value. Thus, “risk” became a common measure of safety. On the other hand, the authenticating organization has evaluated the validation of the determination of the SIL value and the validity of the specific work according to the value of the SIL.
\nThe situation is different in the age of reliance on circuit technology and in the age of use of computers. Nevertheless, the concept of Safety1.0, which seeks to safeguard safety by protection measures in the event of human error or device defect, is common.
\nWhat is important is the fact that computer use is evolving the control system into a more sophisticated one. There is a great difference between the age and today of the development of computerized signaling devices that have solved the issue of how to make hardware and software safe and have replaced safety technology with program logic to produce electronic interlocks and level crossings.
\nThe elimination of concerns about the use of computers for safety control has facilitated the addition of advanced functions. In addition, the successful use of diagnostic technology has opened the way to integrate communication technologies such as networks and wirelesses into safety control devices. The issue of “train control system using IoT” is also due to the fact that communication including wireless communication can be freely used for safety control. Today, the challenge of developing new computer-based systems is continuing, and sophisticated control systems are emerging. In 2011, the world-first full-fledged wireless-train control systems ATACS was launched on the Sensei Line and has been achieving excellent results. Based on this achievement, it was also introduced between Ikebukuro and Omiya on the Saikyo Line in 2016, and has been operating stably. The SPARCS (simple-structure and high-performance ATC by wireless communication system) of radio train control systems developed by Nippon Signal Co. Ltd. is also well received oversea. What are the relationships between these advanced systems and Safety2.0? What kind of system should we look at next to these advanced systems? We would like to consider on the base of the specifications of the computer- and radio-aided train (CARAT) control system in which the author participated in the development when he was in the Railway Research Institute.
\nThe interlocking of the CARAT is called point-control. The position of the train is managed by the block-ID and the position (kilometer) in the block. A plurality of trains can exist in one block except for the section of the point machine in order to realize the movement blocking even in the station premises. Point control does not include path locking or segmented locking. Since the route is pulled back by exchanging information with the on-board device, it is not determined only by the train position. A reasonable and safe process is substituted for the time of the access lock, which was uniformly applied when the line is in the approaching section. The prototype of this point control was installed at Tsubame-Sanjō Station, and the function was confirmed by a monitor run.
\nSince the CARAT was designed to cope with the Shinkansen, the level crossing control function is not required. However, investigation of functions and check of the effect by simulation were carried out, and it was proven to be effective for the fixed-time control and safety improvement, which had been regarded as an issue of the existing level crossing. As a result, the point control and the level crossing control are positioned as processes for extending the point where the train is allowed to travel. The interval control device generates the “traveling permit point information” and transmits the result to the on-board device, thereby making it possible to unify the processing in the interim of the station and in the premises of the station. The form in which each of the emerging devices performs reasonable processing while exchanging information in various directional is precisely located in the IoT. The compatibility between CBTC and Safety2.0 appears to be good.
\nExisting train control systems have condensed know-how learned from the experience of large accidents caused by human error. As shown in Figure 4, the basic control function is the blocking function and the interlocking function. However, safety cannot be ensured by this function alone. Today’s safety is achieved in cooperation with safety devices such as ATS and ATC for the objective of preventing accidents caused by human error. Nevertheless, as shown in Figure 4, the actual situation is a complex combination.
\nAn architecture of conventional train control system.
A simple and orderly system as shown in Figure 5 emerges from the IoT-based train control system. On the scene, there are only point machines, level crossings, and trains that make up the route of travel. The processing unit of the center directly transmits “information up to the section where the vehicle can safely travel” to the on-board device of the train as a control command (travel command). In the CARAT, the interval control device sent the “travel permission point information” to the company office device below the information of the point control. The interval control device is centralized at the center, and at the same time, both the point control and the level crossing control are centralized at the center. Therefore, this form is organized only by adding the IoT viewpoint to that demonstrated in the CARAT.
\nSystem architecture of unified train control system.
The “interlocking device,” “blocking device,” and “ATC/ATS,” which have been so popular in the signal field as to be the three types of gods, disappear. However, it is not the introduction of the “centralized linkage device.” Existing interlocks have incorporated various locking logics to ensure the safety of whatever handling is done by the signal handler. In this respect, the centralized interlocking device does not change at all. Instead, it is claiming to replace the complex interlocking logic itself with point control, which makes it unnecessary. Point-control algorithms have been demonstrated in the monitoring run of the next-generation train control systems CARAT carried out by the Railway Institute on the Joetsu Shinkansen.
\nThe overall system architecture consists of a terminal layer and a center device (functional layer) for controlling site equipment/on-board safety control devices, and an IP network (network layer) connecting them. This level next-generation system is named unified train control system (UTCS), and various studies are being conducted [5, 6]. This system also conforms to the concept of Safety2.0, which consists essentially only of the equipment necessary for the system: trains, point machines, level crossings, and center equipment, and “the essentially necessary equipment exchanges information with each other and realizes functions (I have called this intrinsic control).”
\nIn the UTCS, the concept of a “path” (labeled “authorized route” in Figure 5) for a train is introduced for the standardization of processes. A path means a “limit position to which running is possible,” and is derived from an associated preceding train, a point machine, and the states of a level crossing for each train. For this reason, train processes by unified processors are realized by train tracking, path searching (or “route searching”), and control processes that are initiated by route searching processes in order to control level crossings and point machines.
\nWhen paths for trains are determined, an authorized command with additional speed restriction information in a path is also generated and sent to the corresponding terminal device of the terminal layer. Path searching creates a search for a limit point to which running is possible (a path) in the train movement direction. In the case of station premises, however, a search is made according to a scheduled running path acquired from the running control device (or “traffic control system”) on the functional layer. The path at this time is based on the terminal end of the running path and is determined by the state of point machines existing in between and at the tail position of a possible preceding train (including the safety margin).
\nOn the other hand, in the case of a midway point between stations, the tail position of the preceding train or the state (labeled “status” in Figure 5) of an existing level crossing is associated with the determination of a path. If the level crossing is controlled by the relevant train and the status indicates “passing allowed,” which means closing completion and no obstacle, the search is extended up to a further remote position. Although on-board devices are responsible for on-board safety processing, a continuous speed check according to a pattern is realized on the train anyway. Moreover, in the case of the CBTC, a high-level speed check function can be realized by installing a terminal device on the train, rather than providing an ATP terminal device on the ground. In Figure 6, a data flow under UTCS is illustrated.
\nData flow under UTCS.
An example of a UTCS that relies on Safety2.0 is ATP-block system [4, 5, 6], which is typical of intrinsic control, although the detail of the ATP-block system is omitted, and the essential devices constituting the system mutually exchange information as IoTs, thereby realizing advanced functions. In the case of ATP-block system, the blocking device and the interlocking device, which were previously located at the station and controlling the driving direction with the adjacent station, disappear(see Figures 7, 8) The history of technological progress in a train control system and its relation to the Safety(x,x) is illustrated by Figure 9.
\nComponents of ATP-block system.
System configuration of ATP-block system.
History of technological progress in a train control system and Safety(x.x).
Development of new systems involves certification work in accordance with international standards. Especially in the train control, the train is subjected to the baptism of the standard of the reliability, availability, maintainability and safety (RAMS; IEC62278). To make this baptization smart, it avoids complications and makes the system as simple as possible. Railways are one of the social systems and have a long service life. Erroneous selects can leave the roots of the trouble. Examining and simplifying the components as much as possible improves the system’s visibility and facilitates certification. Furthermore, the reliability is increased by the reduction of the amount of goods, and protection becomes unnecessary. The fewer the number of interfaces, the greater the safety. In addition to the advantages of these nonfunctional requirements, we believe that systematization in accordance with Safety2.0 can be an informative methodology in this regard. Under these circumstances, if information such as orbit protection is automatically extracted by AI based on vehicle vibration data and the like during daily driving, the railway can be reconstructed as a competitive transportation means.
\nSafety2.0 is an initiative that contributes not only to safety effects but also to productivity improvements and contributes to management. I hope that UTCS will be a successful development. We believe that this will also contribute to the dissemination of Japanese Safety2.0.
\nMore than ever, the world has to work together to find renewable energy solutions to combat the Climate Crisis. Since 2015, all UN Member States have committed to ensuring that everyone has access to cheap, dependable, sustainable, and contemporary clean energy by 2030. Clean energy is derived from renewable natural resources such as the sun, wind, tides and waves, and geothermal [1].
Renewable energy sources, such as solar and wind energy, are more volatile than traditional energy sources since they are weather-dependent. As many countries throughout the world expand their renewable energy supply [2, 3], it is critical to ensure that these clean energy sources offer a consistent supply while replacing fossil fuel-based energy sources. The renewable energy applications range from large-scale and off-grid electricity generating (for rural and remote areas) [1] to heating/cooling systems and transportation.
Wind energy is one of the most widely used renewable energy sources, accounting for 4.8 percent of global electricity production in 2018 [4, 5] and 15 percent of Europe’s electricity consumption in 2019 [6]. The mechanical power of the wind is used to power turbines that generate electricity, which generates wind energy. Because wind has a fluctuating intensity over time and might stop blowing at any time, electricity generated by this source is frequently coupled with other power sources to improve reliability and stability.
Wind energy is one of the RES with the lowest electricity production costs and the largest available resource. As a result, a growing number of countries are realizing that wind power offers a great future power generation opportunity.
By dealing with the intermittence characteristic of wind, forecasting methods can improve wind position. Although wind energy cannot currently be dispatched, the financial impacts of wind can be greatly decreased if wind energy can be scheduled using precise wind predictions. As a result, improving wind power output and developing a wind speed forecasting tool has a huge economic and technical impact on the system, Figure 1 detailed classification of deterministic wind speed and power forecasting.
Detailed classification of deterministic wind speed and power forecasting.
A number of institutes and organizations with extensive experience in the subject have dedicated numerous studies to the advancement of wind forecasting techniques. Models like WPMS, WPPT, Prediktor, ARMINES, Previento, and others have been developed and deployed in wind farms all around the world. Physical, statistical, and hybrid methodologies were used to develop these models, Table 1 presents a list of wind power software prediction models developed internationally.
Model name | Developer(s) | Method | Some geographical locations of applications |
---|---|---|---|
Prediktor | L. Landberg at Risø, Denmark | Physical | Spain, Denmark, Republic of Ireland, Northern Ireland, France, Germany, USA, Scotland & Japan |
WPPT | Eltra/Elsam collaboration with Informatics and Mathematical Modeling at Denmarks Tekniske Universities (DTU), Denmark | Statistical | Denmark, Australia, Canada, Republic of Ireland, Holland, Sweden, Greece & Northern Ireland |
Zephyr | Risø & IMM ay DTU, Denmark | Hybrid | Denmark& Australia |
Previento | Oldenburg University | Hybrid | Germany, Northern Ireland |
e-WindTM | True Wind Inc., USA | Hybrid | USA |
Sipreólico | University Carlos III, Madrid, Spain | Statistical | Spain |
WPMS | Institute of solar energy technology (ISET), Germany | Statistical | Germany |
WEPROG | J. Jorgensen & C. Möhrlen at University College Cork | Hybrid | Ireland, Denmark and Germany |
GH Forecaster | Garrad Hassan | Statistical | Greece, Great Britain & USA |
AWPPS | École des Mines, Paris | Statistical | Crete, Madeira, Azores & Ireland |
LocalPred&RegioPred | M. Perez at center national energy renewable (CENER) | Hybrid | Spain and Ireland |
Alea Wind | Aleasoft at the Polytechnic University of Catalonia Spain (UPC) | Statistical | Spain SOWIE Eurowind GmbH, Germany Physical Germany, Austria & Switzerland |
EPREV | Institute of Systems and Computer Engineering of Porto (INESC), Institute of Mechanical Engineering and Industrial Management (INEGI) and Center for the Study of Wind Energy and Atmospheric Flows (CEsA) in Portugal | Statistical | Portugal |
Scirocco | Aeolis Forecasting Services, Netherlands | Hybrid | Netherlands, Germany & Spain |
Presents wind power software models prediction internationally.
In general, wind forecasting is mostly concerned with the immediate-short-term of minutes to hours to commonly up to 1 day and the long-term of up to 2 days. WPMS, as an example of immediate-short-term models, currently predicts wind generation for over 95 percent of Germany’s territory. Reference [5] discusses immediate short-term wind forecasting models. In addition, various models for short-term wind forecasting have been created, such as Predictor, Zephyr, AWPPS, and Ewind, which are all based on high precision numerical weather prediction (NWP) [6, 7]. Previento, which employs a hybrid technique, can anticipate wind for up to 48 hours. References [8, 9] include more studies on long-term forecasting models.
Variations in energy production (induced by variations in wind speed) will become more noticeable on the electrical system as the penetration of wind power generation grows (in terms of the overall energy mix). To avoid balancing concerns, Transmission System Operators (TSOs) operating to balance supply and demand on regional or national grid systems will need to foresee and manage this unpredictability. The moment at which this is necessary varies by system, although it has been noted that it becomes critical when wind energy penetration reaches roughly 5% of installed capacity.
As wind energy’s penetration into individual networks grows, it will be important to make wind farms look more like conventional plants, necessitating the ability to estimate how much energy will be produced over short to medium periods (1 hour to 7 days). Operators, managers, and TSOs commonly anticipate the output from their wind farms in European nations where there is already a substantial level of penetration, such as Spain, Germany, and Denmark. These estimates are used to plan the operations of other factories and for trading.
As the amount of installed capacity develops, forecasting wind energy generation will become more important. The wind industry must expect to do everything possible to enable TSOs to use wind energy to its full potential, which necessitates reliable aggregated output estimates from wind farms.
At the same time as improving the predictability of wind energy plant production through better forecasting tools, it is important to be aware of the true behavior of conventional plants. All of the different energy forms must be considered on an equal level in order to produce the best mix of plants and technologies. As a result, a comprehensive statistical analysis of renewable and conventional plants is critical. This task should be viewed as a critical component of a wind energy development plan, and it should be approached from a comprehensive power system standpoint.
Electricity producers, which include corporations that run wind farms, sell predetermined amounts of energy (measured in kWh) to regional or national energy companies (in the case of wind energy). Because the grid is intended to provide a constant supply of electricity, governments may punish energy producers with large fines if there are power outages.
Energy trade businesses play a critical role in assessing the risk of energy transaction shortfalls by assisting in the forecasting of expected energy production (especially in the case of wind, as a non-steady energy source). Energy dealers, on behalf of energy producers, forecast energy production (in our case, wind energy) using two scenarios:
If there is a shortfall below the forecast, electricity is purchased on the spot market to keep the system running (with prices above the average energy price).
Energy producers are not rewarded for surplus energy produced in excess of predicted output.
In this regard, accurate energy output forecasting is critical to the financial performance of wind farms (i.e. wind energy producers).
The following steps must be completed in order to anticipate the wind farm’s electricity production:
Predict the variation in long-term wind speed over the site at the machine hub height based on long-term wind speeds at the mast locations;
Predict the wake losses that occur when one turbine operates behind another.
In addition to the wind data, the inputs to this process are typically as follows:
Wind farm layout and hub height;
Wind turbine characteristics, such as the power curve (which depicts a turbine’s power production as a function of wind speed) and the thrust curve (which plots the force exerted by the wind at the top of the tower as a function of wind speed);
Air density and turbulence intensity at the place with time (the “gustiness” of the wind).
The topography of the place and its environs; and
Overlay of surface ground cover on the site and in the adjacent region.
Low variability and great predictability are required for a reliable energy source. While the modest variance is acceptable, poor predictability is not, and can result in significant revenue loss. Wind energy fluctuation is caused by a heavy reliance on weather, which varies during the day and annually. As a result, precise weather forecasting is required to produce a useful wind power forecast, Figure 2 shows Dual-step wind power prediction approach based on a hybrid wavelet transform (WT)-ant colony optimization algorithm (ACO)-feedforward artificial neural network (FFANN).
Dual-step wind power prediction approach based on hybrid wavelet transform (WT)-ant colony optimization algorithm (ACO)-feedforward artificial neural network (FFANN).
It is commonly known that the accuracy of weather forecasts improves as the forecast horizon shortens. Combining forecasts from multiple numerical techniques can also be advantageous. As a result, wind farms rely on a variety of weather forecasts given by different models at different times of day or week. Although the weather cannot be controlled, the wind sector may take advantage of advances in artificial intelligence to increase the predictability of the energy supply.
In the chapter, there are several ways for forecasting wind power are categorized as Physical models, statistical models, and hybrid.
Models for wind energy forecasting can be classified into two categories. The first is based on historical wind time series analysis, while the second is based on anticipated values from a numerical weather prediction (NWP) model. However, physical methods, classic statistical or ‘black box’ methods, and more recently, so-called learning approaches, artificial intelligence, or “gray box” methods are used to characterize wind power forecasts. All of these can be included into hybrid approaches.
The first category of models utilizes a statistical approach to anticipate mean hourly wind speed or directly forecast electric power production. To anticipate wind power N-steps ahead, the models in the second category use explanatory variables (often hourly mean wind speed and direction) generated from a meteorological model of wind dynamics. In the majority of cases, the models in the first group produce good results in the estimation of mean monthly or even higher temporal scale (quarterly, annual) wind speed.
However, the influence of atmospheric dynamics becomes more important in the short term (mean daily or hourly wind speed predictions), making the adoption of the second group’s models necessary [10].
In wind power forecasting, there are three steps: first, determining wind speed from a model; second, calculating the wind power output forecast or prediction; and finally, regional forecasting or upscaling or downscaling, which can be implemented over various time horizons. Statistical models are typically used in very short-term forecasting. Ensemble forecasting is utilized to overcome these statistical and learning method conditions [11].
Nielsen et al. [11] demonstrated that if several NWP forecasts are used the forecast error decreases. Louka et al. [12] showed that the Kalman filter can remove systematic forecast errors in NWP wind speed forecasts. Wind forecasting can be separated based on the prediction horizon into three categories:
For short-term forecasting, several tools have been created, including WPPT, Predictor, Zephyr, Ewind, WPFS Ver1.0, and AWPPS. A number of case studies in Spain, Germany, Denmark, Ireland, Greece, and France have used these models [14, 15].
The Wind Power Prediction Tool is a well-known model with a wide range of applications for this time frame (WPPT). It can be used to generate short-term (say, up to 120 hours, or 36 hours) wind power output projections. Because the system can provide prediction values as a total including not only a single wind farm, but also a region, it is extremely flexible. The system also gives accurate estimates of the tools’ uncertainty, which is critical for efficient scheduling or trading. WPPT uses advanced nonlinear techniques.
Because it may produce prediction values as a total spanning not just a particular wind farm, but also a region, the technique is extremely versatile. The system also provides accurate estimations of the tools’ uncertainty, which is critical for optimum trading or scheduling. Advanced nonlinear statistical models underpin WPPT. A semi-parametric power curve model for wind farms that take both wind speed and direction into account, as well as dynamical forecasting models that describe the dynamics of wind power and any diurnal variations, are among the models included in the package. Self-calibrating and self-adaptive models have been developed.
As a result, they update parameters automatically in response to changes in the number of turbines and their features, the environment, the NWP models, and non-explicit model attributes like roughness and filthy blades. WPPT can automatically calibrate to the observed circumstances using artificial intelligence [14]. The system requires online wind power measurements in its simplest configuration. However, the following data is taken into account depending on the configuration: Wind power measurements are now available online. Energy readings from all (or almost all) turbines in a region aggregated (for regional forecasting). Wind speed and direction forecasts by meteorologists for wind farms and regions.
Other measurements or predictions, such as local wind speed, stability, and the number of active turbines, are available. Prediktor, a tool developed by the meteorology research program, is another useful tool (MET). Unlike WPPT, however, Prediktor’s main goal is to represent as much as possible using physical models. Every 6 hours, the system provides the predicted production of wind farms for up to 48 hours. All it requires is online access to NWP model output.
The basic processes are as follows: a NWP model predicts overall weather patterns. Only the entire wind can be predicted by such a model, and only correct forecasts can be made at a given site. Then, if needed, these projections are tailored. The WAsP model tailors the wind turbines to each other by modeling local characteristics such as roughness, horography (ridges and hills), and obstructions, as well as the influence of the wind turbines on each other.
Since no model can simulate nature perfectly, two MOS (model output statistics) filters are used in Prediktor to correct shortcomings. The wind power observed is used to adjust the parameters of these filters. The final output of the model is the expected production of the wind farm every 3 hours over the next 48 hours. Furthermore, Prediktor forecasts or will forecast in the near future for up to 50 wind farms in Ireland, Denmark, Germany, France, and Spain in 2025 [14].
The AWPPS is the only instrument available that estimates confidence intervals for wind power predictions at a predetermined level of certainty (i.e. 85 percent, 90 percent, and 95 percent). The intervals are generated using an important international dedicated to the problem of wind prediction. The Prediction Risk Module allows to forecast uncertainty for the next 24 hours based on projected weather stability. Furthermore, the online use of this module allows for the development of appropriate techniques for optimizing the value of power forecasts [16, 17].
A general overview of wind forecasting models is presented in Table 2. This section is divided into three parts based on the time-scales, and for each of them and its applications.
Time-scale | Range | Applications |
---|---|---|
Immediate-short-term | 8 hours-ahead | • Real-time grid operations |
• Regulation actions | ||
Short-term | Day-ahead | • Economic load dispatch planning |
• Load reasonable decisions | ||
• Operational security in electricity market | ||
Long-term | Multiple-days-ahead | • Maintenance planning |
• Operation management | ||
• Optimal operating cost |
Time-scale classification for wind forecasting [18].
Immediate short-term forecasting Models
Medium-term forecasts (from 6 hours up to a day) are used to make decisions for switching the turbine on or off for safety or conditions on the market.
WPMS has been adapted for performance in the ICT settings of various grid operators and carriers of major wind parks, as one prominent example of immediate-short-term wind forecasting [19].
WPMS deployed artificial neural networks (ANN) in wind farms that were trained using a large amount of historical data. A preprocessor translated input data, output data measured in wind farms, and forecasted meteorological parameters into XML-format before being sent to the program core, which consists of prediction and transformation modules.
Long-term forecasting
Long-term wind forecasting methods have been studied in a few researches. And there aren’t many prediction tools available for this timeframe. Simple models can no longer match the criteria because to the extended ahead-forecasting time, hence NWP or hybrid NWP models are being investigated. Modern wind power forecasting methods, which are typically based on NWP, provide forecasts over a time range of up to several days. To put it another way, the NWP is the source of all information about the future of wind forecasting.
The national weather service or private weather data provider supplies a collection of NWP data that can be used to predict wind speed and power. In the future, it is becoming more common to use NWP for long-term forecasting [20]. Previento is comparable to Prediktor, but it utilizes more severe physical downscaling and specific upscaling techniques. It provides a reliable forecast of projected wind power for any locations and regions in Germany, Europe, and the rest of the world up to 10 days ahead of time, with a temporal resolution of up to 15 minutes. The wind power forecast is based on the best possible mix of meteorological models, as well as the local conditions of the wind farm’s surrounds and the NWP [21].
The Previento system involves a physical approach with data from a large-scale weather prediction model, such as the German Weather Service’s Lokalmodell. It simulates roughness, horography, and wake effects in the boundary layer. The daily variation of the thermal stratification of the atmosphere, which is employed to adjust the logarithmic profile, is critical for calculating wind speed at hub height. The expected power output for single sites is derived using the turbine’s particular power characteristic. The total amount of power generated by wind in a certain region is computed using data from chosen wind farms.
For long-term planning, long-term forecasts (from a day to a week or even a year) are utilized (to schedule the maintenance or unit commitment, optimize the cost of operation). Maintenance of offshore wind farms can be extremely costly, thus proper planning of maintenance activities is essential. Wind power predictions have a temporal resolution of 10 minutes to a few hours (depending on the forecast length). Wind power forecasting improvements are concentrating on using additional data as input to the models involved, as well as offering uncertainty estimates alongside the standard predictions.
Wind forecasting schemes as Figure 3 can also be classified based on their methodology into many categories:
The conceptual mind on wind energy prediction.
Approaching the situation physically (deterministic approach), the physical approach, also known as the deterministic technique, is based on weather forecast data such as temperature, pressure, surface roughness, and obstructions in the lower atmosphere, or numerical weather prediction (NWP).
Established several physical models based on weather data to predict wind speed and estimated wind power [22]. Physical models often rely on global databases of meteorological information or atmospheric mesoscale models, but to provide accurate results, they require massive computer systems [23].
To estimate wind power production, the physical method uses a thorough description of the lower atmosphere. Cellura et al. [22] provide an overview of some of the neural, geostatistical, and hybrid models that have been applied in space-temporal wind forecasting. Dynamic models (also known as prognostic) and kinematic models (also known as diagnostic) are the two main forms of numerical codes for wind field modeling across rugged terrain [24, 25]. The momentum and energy equations are not explicitly solved in these models; instead, parametric relations and/or wind data are used to examine them implicitly [26].
To account for the local circumstances of the physical topography, computational fluid dynamics (CFD) is utilized as an alternative to the power law [27]. Model output statistics (MOS) are frequently employed to reduce systematic forecasting mistakes and to compensate for unknowns in the expected power output [28].
Forecasts are provided at specified nodes on a grid that covers a certain area. Due to the fact that wind farms are not located on these nodes, these estimates must be extrapolated to the required location and turbine hub height. Physical-based forecasting methods are comprised of multiple sub-models that work together to translate wind forecasts at various grid points and model levels to power forecasts at the actual site.
Converting wind speed to power at the level of the wind farm and at hub height depending on the using theoretical power curves supplied by the wind turbine manufacturer. However, since multiple studies have demonstrated a preference for empirically obtained power curves over theoretical ones; theoretical power curves are becoming less and less important. When using a physical methodology, the function that calculates wind generation from NWPs at various locations around the wind farm is modeled once and for all. The calculated transfer function is then applied to the current weather predictions. Physical simulations frequently integrate Model Output Statistics (MOS) for post-processing power forecasts to account for systematic forecasting errors that may be due to the NWP model or modeling approach, Figure 4 shows steps forecasting wind farm with NPW.
Steps forecasting wind farm with NPW.
Statistical approach statistical method is based on the vast amount of historical data without considering meteorological conditions. It usually involved artificial intelligence (neural networks, neuron-fuzzy networks) and time series analysis approaches [29, 30]. Statistical models, the set of models includes a semi-parametric power curve model for wind farms taking into account both wind speed and direction, and dynamical forecasting models describing the dynamics of the wind power and any weather variation, etc.
Statistical forecasting approaches are based on one or more models that establish the relationship between historical power values, historical and future values of meteorological variables, and wind power measurements. The physical events are not deconstructed and accounted for, despite the fact that problem expertise is required for selecting the appropriate meteorological variables and developing appropriate models.
Model parameters are calculated using a collection of previously known data, and they are updated on a frequent basis during online operation to account for any new information that becomes available (i.e. meteorological forecasts and power measurements).
Linear and nonlinear statistical models, as well as structural and black-box models, are all examples of statistical models. Structural models rely on the analyst’s knowledge of the phenomenon of interest, whereas black-box models are built from data in a fairly mechanical manner and require little subject-matter knowledge.
Structural models for wind power forecasting would include diurnal wind speed changes modeling or an explicit function of meteorological variable predictions. Neural-Networks (NNs) and Support Vector Machines are examples of black-box models (SVMs). Some models, on the other hand, are ‘in-between’ the extremes of being entirely structural or completely black-box. Expert systems, for example, learn from experience (from a dataset) and can be programmed with prior information. The subject of gray-box modeling is then discussed.
Statistical models are often made up of two parts: an autoregressive portion for capturing the wind’s persistent behavior, and a “meteorological” part for nonlinear transformation of meteorological variable projections. The autoregressive component provides for considerable gains in forecast accuracy across horizons up to 6–10 hours ahead, when the use of meteorological forecast information alone may not be adequate to exceed persistence.
Statistical approaches to wind power prediction are currently focusing on the use of multiple meteorological forecasts as input and forecast combination, as well as the best use of spatially distributed measurement data for prediction error correction or issuing warnings on potentially large uncertainty.
Calculate a statistical relationship between the essential input data and the generation of wind energy. They entail utilizing a statistical model to directly turn the input factors into wind generation. With these models, a one-step direct calculation of wind power from input parameters is achievable. Most data mining-based models (e.g., ANN, SVM, fuzzy model, model trees), as well as time series analysis methods, can be used as output models (e.g. ARIMA, fractional ARIMA).
A massive quantity of data is processed in the statistical technique, and meteorological processes are not clearly represented. The relationship between historical power output and weather is established, and this information is then used to anticipate future power output. Statistical methods, unlike physical methods, simply require one step to convert input variables to power output. As a result, the procedures used are referred to as “black box.” In most cases, a statistical relationship is established between the weather forecast or projection and the wind farm’s prospective power output. Other statistical approaches employed include the Box-Jenkins methodology, the use of the Kalman filter, and the use of autoregressive (AR), moving average (MA), autoregressive moving average model (ARMA), and autoregressive integrated moving average model (ARIMA).
Torres et al. [29] discovered that compared to persistence, it was possible to get a 20% error reduction when forecasting average hourly wind speed for a 10 h forecast horizon at a number of locations using nine variables.
Classical time series analysis is not the only approach to model a statistical relationship between data points. Artificial neural networks (ANN) and fuzzy systems are the most common soft computing (or machine learning) techniques utilized, however other models such as gray predictors and support vector machines (SVM) have also been used. Artificial intelligence (AI) approaches are another term for learning approaches. They’re known as learning techniques since they take historical time series to learn about the relationship between projected wind and predicted power production. They’ve been dubbed “gray box” approaches in recent years.
The presentation of parametric statistical methods directly inspired from the physical equation. Parametric modeling according to the wind speed only, the investigated the simplest parametric models, namely linear regression and logistic regression, with the wind speed as the unique explanatory variable. If the predicted power at time t is denoted by Yˆt, these models are given by
where the parameters a0, a1, C are estimated using the associated methodology.
Logistic regression has also been considered to mimic more closely Eq. (1). More precisely, the model is then defined by:
where ai, i = 0, …, 3 and C are estimated parameters.
This model is using not only wind speed as a predictor, but also wind direction, (coded by its cosine and sine: Dcos and Dsin), temperature T, and the variances of the wind speed WS and direction, DS, Re and DS, Im.
The Lasso method, which simultaneously performs variable selection and regularization through the least squares criterion penalized by the ` 1 norm of the regression coefficients has been investigated as well (see for instance [20]). The model is defined by.
with a0, …, a7 minimizing.
Hybrid method, which combines physical methods and statistical methods particularly uses weather forecasts and time series analysis.
ANEMOS is a hybrid wind forecast tool that takes into account a variety of time horizons. The development of combining high-resolution meteorological predictions and appropriate prediction models for the offshore is emphasized [18, 30].
Hybrid models aim to combine the advantages of each model in order to produce the best predicting results possible. Because the information provided in individual forecasting techniques is restricted, a hybrid approach can take use of the available data, integrate individual model data, and maximize the benefits of many forecasting methods, improving prediction accuracy [31].
Many techniques, such as mixing physical and statistical procedures or short-term and medium-term models, are included in hybrid methods. A number of hybrid models were utilized to anticipate wind power. Here are some examples of potential combinations:
A combination of physical and artificial intelligence approaches.
Using a combination of artificial intelligence models Zhao et al. [32] looked into a hybrid wind forecasting system that included both NWP and ANN models. To anticipate meteorological characteristics, the NWP model combines the Global Forecasting System (GFS) with the Weather Research and Forecasting (WRF) system. Figure 5 Shows an example of an ANN structure with 4 inputs and 2 hidden layer.
ANN structure with 4 inputs and 2 hidden layer.
Shi et al. [33] proposed two hybrid models for wind speed and power forecasting: ARIMA-ANN and ARIMA-SVM. Based on two case studies on wind speed and wind power generation, this research analyses the application of the suggested hybrid models in a systematic and thorough manner. The findings imply that hybrid approaches are feasible alternatives for predicting both wind speed and wind power generation time series, but that they do not always provide better forecasting performance for all forecasting time horizons investigated.
Guo et al. [34] proposed a novel hybrid wind speed forecasting method based on a back propagation neural network and the notion of seasonal exponential adjustment to exclude seasonal effects from real wind speed datasets. A proposed technique outperformed the single back propagation neural network in the tests.
For short-term wind power forecasting in Portugal, Catalo et al. [18] presented a hybrid approach based on the combination of ANN and wavelet transform. To deconstruct the wind power series into a set of better-behaved constituent series, the wavelet transform is applied. The test findings show that the proposed hybrid technique for forecasting wind output has a lot of potential.
Finally, hybrid models (e.g. [18, 35]) are based on the combination of the physical and statistical models, the combination of models with several time horizons, and the combination of alternative statistical models
The spatial correlation models take into account the spatial link between wind speeds at different sites. The wind speed time-series of the projected point and its neighbors is used to predict the wind speed in spatial correlation models [35]. When predicting wind speed at one location based on observations taken at another, a spatial correlation model is used. Data obtained over a seven-year period [36] was used to test its behavior and provide adequate verification.
Based on cross-correlation at surrounding sites, Alexiadis et al. [37] demonstrated a technique for forecasting wind speed and power output up to several hours ahead. This research established an ANN technique based on spatial correlation models that outperform the persistence forecasting model in terms of forecasting accuracy [38].
Barbounis and Theocharis [39] proposed the use of local feedback dynamic fuzzy neural network (LF-DFNN) to forecast wind speed using spatial correlation. Remote meteorological stations are installed at two reference sites in accordance with the location of the base site so that the three sites are aligned along the prevailing wind direction. Using spatial information from remote meteorological stations, the LF-DFNN is used in this paper to predict multi-step forward wind speed in the base site. The LF-DFNN outperforms other network models tested in this application, according to simulation data.
Various novel AI algorithms for wind speed and power prediction have recently been developed as a result of the advancement of artificial intelligence (AI). Artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), fuzzy logic approaches, support vector machine (SVM), neuro-fuzzy network, and evolutionary optimization algorithms are among the newly developed methodologies.
Through the training process, ANN models can represent a complex nonlinear relationship and extract the dependency between variables [39]. Back propagation neural networks, recurrent neural networks, radial basis function (RBF) neural networks, ridgelet neural networks, and adaptive linear element neural networks are examples of ANN-based methods. The application of an ANN-based method to the problem of wind power forecasting is appropriate.
ANN might handle nonlinear and complex scenarios in terms of categorization or forecasting. ANN models can depict a complex nonlinear relationship and extract the link between variables through the training phase [39]. Examples of ANN-based techniques include back propagation neural networks, recurrent neural networks, radial basis function (RBF) neural networks, ridgelet neural networks, and adaptive linear element neural networks. It appears that applying an ANN-based technique to the problem of wind power forecasting is a good idea.
Using time series analysis, Sfetsos [40] proposed an ANN technique for forecasting mean hourly wind speed data. The proposed methodology also has a benefit for utilities that have a high level of wind penetration and utilize hourly intervals for power system operational procedures like economic dispatch and unit commitment.
Chang [41] discussed back propagation neural network-based wind power forecasting algorithms. The created model for short-term wind forecasting demonstrated excellent accuracy when utilized to supply energy to a 2400 kW (WECS) on the Taichung coast. Back propagation neural networks and recurrent neural networks were used in More and Deo’s [42] wind forecasting methodology. Traditional statistical time series analysis has been found to be less accurate than neural network forecasting [43].
Chang [44] described a method for forecasting wind power generation time series using an RBF neural network. The numerical results show that the suggested forecasting method is accurate and dependable, with good matches between realistic values and predicting values.
Guo et al. [45] studied a feed-forward neural network (FNN) wind forecasting approach based on modified empirical mode decomposition (EMD). Through multi-step forecasting of mean monthly and daily wind speeds in Zhangye, China, the proposed technique outperforms basic FNN and unmodified EMD-based FNN [46]. Li and Shi [47] used three types of conventional ANNs to anticipate wind speed: adaptive linear element, back propagation, and radial basis function.
The outcomes of comparing three types of ANN reveal that no single ANN model outperforms another universally in terms of all evaluation measures, even for the same wind dataset. Furthermore, the type of ANN to use for the best results is determined by the data sources.
Yang et al. [48] proposed an ANFIS approach for interpolating missing and incorrect wind data. Twelve measured wind data sets from a wind farm in North China are interpolated and examined for performance testing. The ANFIS method’s effectiveness was demonstrated by the test results. A SVM-based technique for wind power forecasting was described by Zeng and Qiao [31]. Real wind speed and wind power data obtained from the National Renewable Energy Laboratory are used in simulation research.
The suggested SVM method outperforms the persistence model and the RBF neural network-based model, according to the results. For one-step ahead, wind speed forecasting, Zhou et al. [49] described a systematic investigation on fine-tuning least-squares support vector machines (LSSVM) model parameters. Three SVM kernels are implemented: linear, Gaussian, and polynomial kernels. LSSVM approaches are proven to outperform the persistence model in the vast majority of scenarios. For short-term wind power forecasting, Xia et al. [50] introduced a neuro-fuzzy network technique.
For the wind power forecasting of a practical wind farm in China, the forecasting approach is used. The results of the tests revealed that the trained neuro-fuzzy networks are capable of predicting and forecasting wind power.
Jursa and Rohrig [51] proposed a new short-term prediction technique based on the automated specification of neural networks and the nearest
Wind Forecasting in the Future The forecast accuracy of wind power prediction systems is becoming increasingly significant due to the high penetration of wind power in the energy grid. Many academics have been working on wind power forecasting in recent years. Forecast accuracy has steadily increased and intensive research and development efforts are projected to be underway soon. In order to improve wind power projections even more, various literature [32, 52] suggest that future studies should focus on the following areas:
Research new artificial intelligence approaches and enhance training algorithms in order to increase forecast accuracy. Future studies will also focus on new strategies for dealing with complicated terrain.
More research into hybrid methods is needed to combine different approaches, such as combining physical and statistical approaches, to achieve good results in both long-term and short-term prediction.
In actual WECS, the existing forecast approach should be used. Continue your investigation into the practical application of the methodologies rather than just the theory.
Develop a more precise assessment methodology and a standard for measuring technique performance.
Improved input data for wind power forecasting will come from improved NWP models and more regular weather forecast updates.
Expand research into the use of online wind data, particularly for short-term wind forecasting.
More study on adaptive parameter estimation is needed. The models can respond to changes in the farms and their surroundings automatically.
Conduct additional research into the NWP models designed for use in an offshore environment. Improve meteorological data available to evaluate NWP results for offshore areas.
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We characterized the optical wireless communication channel through the channel measurements and present different models for the OWC link performance evaluations. In addition, we present some technologies for the OWC performance enhancement in order to address the last-mile transmission bottleneck of the system efficiently. The technologies can be of great help in alleviating the stringent requirement by the cloud radio access network (C-RAN) backhaul/fronthaul as well as in the evolution toward an efficient backhaul/fronthaul for the 5G network. Furthermore, we present a proof-of-concept experiment in order to demonstrate and evaluate high capacity/flexible coherent PON and OWC links for different network configurations in the terrestrial links. To achieve this, we employ advanced modulation format and digital signal processing (DSP) techniques in the offline and real-time mode of the operation. The proposed configuration has the capability to support different applications, services, and multiple operators over a shared optical fiber infrastructure.",book:{id:"6037",slug:"optical-communication-technology",title:"Optical Communication Technology",fullTitle:"Optical Communication Technology"},signatures:"Isiaka Alimi, Ali Shahpari, Artur Sousa, Ricardo Ferreira, Paulo\nMonteiro and António Teixeira",authors:[{id:"205656",title:"Dr.",name:"Ali",middleName:null,surname:"Shahpari",slug:"ali-shahpari",fullName:"Ali Shahpari"},{id:"208236",title:"Dr.",name:"Isiaka",middleName:"Ajewale",surname:"Alimi",slug:"isiaka-alimi",fullName:"Isiaka Alimi"},{id:"208239",title:"Dr.",name:"Artur",middleName:"Neves E",surname:"Sousa",slug:"artur-sousa",fullName:"Artur Sousa"},{id:"208240",title:"Mr.",name:"Ricardo",middleName:null,surname:"Ferreira",slug:"ricardo-ferreira",fullName:"Ricardo Ferreira"},{id:"208241",title:"Dr.",name:"Paulo P.",middleName:null,surname:"Monteiro",slug:"paulo-p.-monteiro",fullName:"Paulo P. 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Wireless power transmission (WPT) technology was first pursued by Tesla over a century ago. However, it faced several challenges for deployment in real applications. Recently, energy harvesting and WPT technologies have received much attention as a clean and renewable power source. Rectenna (rectifying antenna) system can be used for remotely charging batteries in several sensor networks at internet of things (IoT) applications as commonly used in smart buildings, implanted medical devices and automotive applications. Rectenna, which is used to convert from RF energy to usable DC electrical energy, is mainly a combination between a receiving antenna and a rectifier circuit. This chapter will present several designs for single and multiband rectennas with different characteristics for energy harvesting applications. Single and multiband antennas as well as rectifier circuits with matching networks are introduced for complete successful rectenna circuit models. 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obtained an MD from Tehran University of Medical Sciences, Iran. He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. 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Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. 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