\r\n\tThis book will mainly cover work related to: (i) cells mechanosensing and mechanotransduction mechanisms (ii) computational and experimental techniques in mechanobiology, (iii) mathematical mechanobiological models of bone remodeling, (iv) bone mechano-transduction, (v) innovative tools for mechanobiology and the role of medical imaging in this field and (vi) any other proposals related to innovations, clinical application and perspectives of mechanobiology.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"0a38ccecc83b50d8b015a6dd2533049d",bookSignature:"Prof. Abdelwahed Barkaoui",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10255.jpg",keywords:"Nuclear Mechanotransduction, Mechanosensitivity, Fluids Mechanics, Multiscale Mechanobiology, Modeling Cellular Mechanics, Finite Elements Method, Bone Remodeling, Mechanics Stimulus, Multi-scale Modeling, Mechanobiology Tools, Cell Imaging, Cell-Substrate Interactions",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 2nd 2020",dateEndSecondStepPublish:"July 23rd 2020",dateEndThirdStepPublish:"September 21st 2020",dateEndFourthStepPublish:"December 10th 2020",dateEndFifthStepPublish:"February 8th 2021",remainingDaysToSecondStep:"9 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Assistant director of LERMA laboratory, head of mechanical discipline at ECINE and coordinator of the ECINE study program accreditation committee, a member of the editorial board of several international scientific journals, also a member of the American Society of Mechanical (ASME) Engineers European Society of Biomechanics (ESB) and the International Society of Biomechanics (ISB).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"320631",title:"Dr.",name:"Abdelwahed",middleName:null,surname:"Barkaoui",slug:"abdelwahed-barkaoui",fullName:"Abdelwahed Barkaoui",profilePictureURL:"https://mts.intechopen.com/storage/users/320631/images/system/320631.jpg",biography:"Abdelwahed Barkaoui is an associate professor of Mechanical Engineering at International University of Rabat. He obtained his University habilitation from university of Tunis El Manar-Tunisia in 2017 and his PhD from university of Orleans-France 2012. He has a master's degree in mechanics obtained from the INSA of Lyon-France. He held a position of director of the department of sciences and techniques of engineers and was a Member of the Scientific Advisory Board of the Preparatory Institute for Engineering Studies of El Manar. Currently, Dr. Barkaoui is an assistant director of LERMA laboratory, head of mechanical discipline at ECINE and coordinator of the ECINE study program accreditation committee. His research mainly concerns problems in biomechanics, mechanobiology and biomedical engineering. 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The more the knowledge advances, the more the subfield growths and thus the belonging matter struggle. Acoustics comprehends a numerous profession such as architecture, engineering, physics, electronics, speech communication, mechanical engineering, medicine and speech communication, music and arts, and oceanography. Acoustics is a title, which today needs to be more complex as terminology, as all of the mentioned professions consider it as their own.
\nAcoustics is associated until a near date to the engineering. However, nowadays, many titles raised to identify the stipulation of the specific field better.
\nThe soundscape is the acoustics that deals with the unconscious effects of sounds and the surrounding sound background having precise psychological effects.
\nAcoustical environment is the interior design application of the sound behavior within the interior and the different physical reactions on the human functions. Acoustical oceanography is the study of the underwater sound. Acoustical engineering, like science, belonged to the engineering; then by the advancement of information, it splits to mechanical waves, where the science of sound and vibration across the technology exists. Acoustical engineering, as well, has a direct relationship with the sound control through electrical devices.
\nInterior acoustical design cooperates some of the previous elucidated aspects, the soundscape, the acoustical environment, and the electrical sound systems, where all are integrated into the interior spaces based on their functions.
\nWhen and how does humanity recognize acoustics?
\nThe applications of the acoustics start from the historical eras for a diversity of purposes. From the prehistorical eras to the modern decades, especially with the emerging of the science and the recent innovations, the science of sound focused on informing, healing, and amusing. The main purpose of acoustics in design is the hearing of the required sounds. The diversity of disciplines, which deal with the acoustics, arises through some fields. R. Bruce Lindsay created “Lindsay’s Wheel of Acoustics” (Figure 1) [1]. This wheel shows the fields of acoustics starting with the four broad fields of earth sciences, engineering, life sciences, and the arts. The outer circle lists the various broad disciplines one may study to prepare for a career in acoustics. The inner circle lists the fields within acoustics to which many fields naturally lead. The highlighted area shows the room acoustics, or the interior acoustical design, as well as all the related fields affecting to some extent the interior acoustical design clarified within this chapter.
\nLindsay’s wheel of acoustics.
Acoustic engineering is the branch of engineering dealing with sound and vibration, on its physical aspects. It is the application of the science of sound and vibration, in technology, typically concerned with the design, analysis, and control of sound. It also covers additional uses of sound, from the use of ultrasound in medicine to the programming of digital sound synthesizers. Acoustics, like engineering, deals with the electroacoustic elements, which are the electrical sound systems (microphones, headphones, loudspeakers, sound systems, sound masking, noise control, recording, and reproduction of sound). Acoustical engineer interprets with the sound from the physics perception. However, this side of the acoustics does affect the interior, as part of the whole design.
\nA long time before the recent advancements and technologies, the different civilizations managed the available science they acquired from their previous ancestors and developed their daily life needs. By surfing among the different historical eras, we will discover that they had many solutions to solve their acoustical needs. Some of these solutions were, as per nowadays rules, architectural, and others were interiors!
\nFrom the prehistorical to the modern eras, diverse interpretation were used to enhance the sound level and the sound projection within interior spaces for the benefits of the event. Most of these events were educational, and their purpose was informing the communities with specific attained knowledge.
\nAt the beginning of civilizations, the hunters, once back to the village, reproduce the hunting scenes for their young generation to teach them the procedures and tactics. They start to realize that the circular layout is the best to make others see and hear the message. They meet in a circular layout; either they are in the open air or their tents (Figure 2).
\nHunting scenes in the prehistorical era, in tents and open air [
By the Greek and Roman eras, the communities’ fascination was the philosophy and the poetry. They build the amphitheaters, where speech and music could act, to communicate their knowledge. The shape of these buildings was to enhance the sound dissemination within the space (Figure 3). They used the open air to add extra reinforcement to the sound projection. At the same time, the open air works as a reflection to amplify their speech sound, in addition to the “horseshoe” plan layout and the level in the amphitheater style of interior layout, which enhances the acoustic performance of the place [4]. The steps of the audience area act as amplifier additionally to the rule of “Seeing ensures Hearing.” As much, the person sees the stage as much the sound reaches the person without obstacles, although it needs some reinforcements. The high edifice was using the background of the stage performance as backstage services spaces, stage set design, and main reflection elements to strengthen the sound on the stage.
\nThe amphitheater (the Greek Odeon of Herodes Atticus in Greece on the right and the Roman Theater of Mérida in Spain on the left) [
The Islamic civilizations lasted for many eras and were a mix of religious reverence, science, and art lifestyle. They used the sciences as a base to all their inventions and creations under the spiritual beliefs. The great palace of Ālī Qāpū relieves new challenge of acoustical behavior and treatments. Built in the early Safavid style was the six-story Ceremony Hall (Figure 4). The muqarnas and cutout designs function to decrease reflections and act as a sound diffuser for the enhancement of the religious songs without echo, in beauty and functionality. Due to the distinguished advancements of mathematics, especially the algebra and physics, the results are magnificent [5].
\nThe six-story ceremony hall in Ālī Qāpū palace in Isfahan, Iran.
Architectural acoustic design starts from the acoustical engineering. The connection between the physics and their applications when Wallace Clement Sabine, father of the modern building acoustics, gets the attention of the sound in the different lecture halls in Harvard where he studied physics science. Absorption formula title—Sabine’s Formula—honoring his hard studies and experiments to reach today science the Acoustical Design in Buildings. The acoustics of buildings face several obstacles: the modern design trends, the type of materials, the mechanical systems in the buildings, and the surrounding level of noise.
\nThe modern trends in design open up the spaces, no matter the functions and the privacy required for specific zones. Accordingly, the masking noise between the interface and the user concentration disappears.
\nThe building materials become economic in terms of structural properties in favor of space and cost saving. Such products create extra load on the noise production and prevent a safe environment for the users.
\nMechanical systems in the buildings are necessary with today’s climate. The air-conditioning systems, the elevators, and the ventilation are all components that contribute to the noise pattern of the buildings.
\nThe level of noise’s pollution created by today’s lifestyle that is transmitted to the building add additional problems and multiply to a disastrous extend the noise pollution of our environment.
\nThese problems are the main concern of the architectural acousticians.
\nInterior design, as specialization, dates to 1890. Elsie De Wolf developed the Colony Club in the US, and from then, it becomes a profession. The architect—the worker’ supervisor in Latin—was in charge of all the building details, including the interior. Well, the interior design is the profession that cares, protects, and sustains the human life in a healthy, green, and safe environment. The interior acoustical intermediate that is appropriate to the function, reaching the audience in proper level to success the communication, without disturbing or even harming the users hearing, is a prosperous acoustical design. The classification of the interior spaces could lead to a better understanding of the role of the acoustical design to succeed in the functionality of the space. The interior acoustical design differs from the spaces where speech intelligibility is necessary to the spaces where quietness is vital, to spaces where the music needs enhancements, to spaces of private communications, and to spaces where public announcements take places. Each of these categories will require a specific combination of materiality schemes that balance between the physical properties and their quantities to provide an adequate acoustical environment [6].
\nSpeech clearness is vital in a specific type of functions. Without speech intelligibility, classes’ success could fail. Teacher explanations, court sessions, and board meetings are functional, practical examples where speech is the key role of their success. Large institutions need clear discussions to make efficient decisions. In the court, when the speech is not clear, many judicial problems could take part from innocently. Such spaces need clear speech with no reflection nor echoes. In such functions, the number of absorber materials should increase in favor of the reflectors. The absorption material scheme when exceed, will produce a harmful background and thus will require more effort from the sources to enable the audience to hear. Therefore the introduction of some diffusers is necessary to balance the acoustics performance (Figure 5).
\nExamples of classroom, boardroom, and court room where the speech intelligibility needs many absorbers.
Sick peoples are the top users of quiet spaces where they take their recovering period. Museums are the place for specific levels of emotional entertainment or educational session; both of them are a relatively individual requirement and need high quietness levels. Classrooms are the spaces where instructors and students met for the educational processes environment. All these spaces (Figure 6) do require the best level of quietness background to achieve their specific functions. Hospitals, museums, and classrooms need a quiet environment. Such acoustical needs require more than the interior material scheme, it needs the isolation treatments to reach the quietness level.
\nExamples of museum, hospital, and living room where the quietness needs much absorbers and isolations.
Recording studios, musical halls, and theaters are the locations of musical enhancement functions. Each of these areas needs different acoustical treatments to reach a vital level of sound. Studios need absorption layout to remove any reflection possible. While the musical halls, where live music takes place, requisite a mixture between the reflection and diffusing materials to reach the life feeling of sound without echoes nor masking of sound (Figure 7). Theaters are relatively difficult as they could host drama performance and musical performance.
\nExamples of concert hall where the music needs to be enhanced through the reflectors and the diffusers.
Private discussion occurs in human resources offices, in a medical clinic, in residential spaces, and police stations. These environments need high absorption quality to promote the privacy necessary. One of the human feelings is to feel shame from exposing private issues. Respecting and protecting these feelings are mandatory in designing such spaces. Such spaces could exist in buildings where other acoustical requirements are in question. Example of Doctor Room that needs privacy, during the clinic, and called for the quiet environment.
\nAirport, public malls, and governmental spaces for individual services are all spaces where public announcement occurs. The announcement of the flight number and timing requires a clearness in the speech to understand the announced information. Otherwise, the disruption and the distraction are the results. Similar results could happen in the public spaces as malls or governmental facilities (Figure 8). Acoustic design plays a different role in creating a masking background to clear the pronounced data.
\nPublic announcement (main corridor in a mall where absorber patterns in the ceiling on the right and principal hall in airport where absorber baffles on the left).
Interior acoustics support the well-being of the users. The design of the spaces that require a sound quality needs specific elements to embolden the acoustical quality. The interior acoustical elements involve and interlace with other acoustical fields. These elements start by the space layout, which should be architecturally created to the specific function to prevent any basic problems. Otherwise, the interior solutions could cover such deficiency. The selective materials are the essence of the interior acoustical design. The interior treatments, quality, and position play the main role in the excellence of the acoustical functionality. It is a pure interior design, although the architect and especially the architectural acoustician consider it, to a very recent date, architectural treatments. The acoustical sound systems come as the third element as it is the additional element to any acoustical interior, for specific targets. If the acoustical interior design reflects the user’s function, the acoustical engineering solutions presented through the sound systems should have a specific objective.
\nThe sound design, as a background, affects the users unconsciously. It could contribute to the psychological mood of the users. Human is productive while listening to his/her favorite music by 30%. The type of sound background—or the soundscape—contributes in the overall success of the interior functions. Slow music accelerates the blood circulation, therefore speeding up human functionality. On the other hand, the slow music depresses most of the time the human being. Soundscape, as a new psychological tool, bolsters in today interior design upgrading the overall interior purposes.
\nThe interior acoustical design is a combination of these three important elements that create the interior spaces in excellence [7].
\nThe architectural design affects deeply in the interior sound behavior. While the sound behavior follows the architectural layout, the architectural shape could lead to a successful acoustic or a harmful one. The architecture should support the acoustical design to enhance the functionality rather than create problems that need extra solutions. Domes, circular plan, parallel surfaces, and unproportioned spaces lead to several acoustical problems for the interior spaces. Solving these problems will over cost the interior, as the acoustical treatments are expensive as raw materials and as fire-resistant coatings [8].
\nDome does reflect the sound to a specific point, creating a nonstop echo. Adding absorbers on the full surfaces of the dome will create a convenient sound layout for space. Suspending of a huge luminaire, designed for masking the echo, is a solution of cutting out the reflection but in condition to use an amount of absorbing materials within its design. Baffles are a different solution but, similar to the suspended luminaire, it will block the view of the dome and will create an additional different perception.
\nCircular plan is a challenge for the acoustical design perfection. Similar to some extent to the dome, straight surfaces of paneling would break the echo resulting from the circular perimeter. A mixture of balanced acoustical materiality scheme is the best solution, and the decision of the properties of the materials relates to the functional acoustical study of the specific interior [9].
\nThe interior surfaces should not have parallel surfaces, either as peripheral surfaces or as ceiling and flooring opposite to each other. The interior acoustical designer needs to break these parallelisms. The creation of acoustical interior treatments, diverse in quantities and properties, will solve this problem, although it is much expensive than having the spaces without such problems.
\nThe sound behavior in the interior spaces is the result of the interior treatments used. The quality and the properties of these materials share to some extent the success of the sound propagation within the space. The sound could be reflected, absorbed, diffused, or transmitted depending on the material physics (Figure 9). Materials used in the acoustical design are expensive as their majority are natural materials. Additionally, they need exclusive fire-resistant coating.
\nSound behavior in the enclosed spaces ((1) sound wave projected from the source, (2) sound wave reflected on the surface, (3) sound wave absorbed, and (4) sound wave transmitted).
Reflectors are hard solid materials that reflect the sound following the rule of reflection—angle of projection equals to angle of reflection but in the opposite side. Best reflectors are the ones made from natural hardwood with thickness limits of 5–15 cm (Figure 10). Oak, beech, mahogany, maple, walnut, and pine are all good selection. Ash wood is one of the best as it can easily bend which allows better coverage in favor of materiality limitations. Convex surfaces will scatter the sound waves allowing natural reinforcement for the unamplified voice without electrical sound systems while reducing the material surfaces.
\nReflectors appearance in the interiors on the left and the difference of dimensions between the flat reflectors in comparison to the convex reflectors on the right.
Opposite to the reflectors, absorbers are soft, porous materials (Figure 11). Used to name the foams with different densities and thicknesses, a sandwich panel composed from a finished layer of fabric or soft natural perforated or slotted wood (with different perforation percentages), and the inside might be one or more of the following materials: rock wool, glass wool, or foam, based on its absorption coefficient “alpha”. Heavy curtains exceed its dimensions seven times the length to work as absorbers.
\nFrom left, perforated and slotted softwood, sandwich panels (fabrics coverage with glass wool, rock wool, or foam), and different densities of foam.
Diffusers are the type of treatments that deploy the sound wave in a way to keep it alive without reflecting it to a specific spot nor to absorb it (Figure 12). Diffusers are similar to the reflector properties
Diffusers in different appearances of curves and angles, through the hard solid selection.
The isolation materials usually are used to prevent the transmission of the sound waves from space to another. Spaces could be enclosed or open, but the essential is to block their flows and thus avoid the noise transmissions. Isolation materials consist of high-density absorbing materials in addition to reflectors in a composition that supports, scientifically and structurally, the sound blockage. However, they are part of the structure of the building layout, yet the acoustical interior may interface to solve specific noise transmission and therefore be part of the design. Building materials are the rescue to solve such situation in the interior acoustical design phases.
\nElectrical sound systems for the acoustical interior design are not useful, especially when the interior wall is acoustically designed. It has dedicated purposes when recording constituents play a role in the functional aspects. It is worth to mention that this section is an acoustical engineering field that serves the interior and its inhabitants directly. For the acoustical interior designer, the sound system exposure to three main questionings, the what, the why, and the how of the electrical sound system [10].
\nWhat are the sound system and its components? Why should we use the sound system? How to apply the sound system within ideal regulations?
\nWhat are the sound system and its components? The sound system encompasses three essential elements: the microphone, the loudspeakers, and the controlling devices (Figure 13). Microphones are diverse in shape, design, and specifications. Each serves a specific function. The self-switch, the controllable, and the standard normal are all options for the specific situation in the overall interior function and needs. Electrically spoken, the types of microphone differ based on the frequency response, internal circuitry, resistance to the moisture, diaphragm size, weight, and durability. Concerning the loudspeaker, it consists of main parts: the woofer for the low frequencies, twitter for the high frequencies, and the midrange. Finally, the control panel to amplify the sound based on the specific results is required [11].
\nSound system components (microphone, loudspeakers, and control panel).
Why should we use the sound system? The sound behavior in the interior spaces is the result of the interior treatments used. The quality and the properties of these materials share to some extent the overall quality of the sound. Using the sound system is for recording the event (unrepeated or memorial event), for listening to recorded materials (like the cinema), and for the simultaneous translation. Except for these three purposes, the designer does not refer to the sound system since the interior which follows the standard setup will never necessitate such devices.
\nHow to apply the sound system within ideal regulations? The sound system should follow strict regulations to ensure the quality level of the sound projected. Therefore, the strict regulations (Figure 14), once applied, will provide the best sound system results. The selection of sound system properties contributes to the overall design of these sound systems in the interior layout. The angles of the sound projections from the loudspeakers determine the numbers of speakers and their positions in the interiors.
\nSound system regulations in a section layout [
The international meeting and event necessity are the simultaneous translation. In this case, the translation booths should be available by numbers equal to the six official international languages. These booths should be enough in sizes to host, in each, two translators—each takes speech for 10 min. The visual field of these booths in the space must be clear to the main panel of discussion on the stage.
\nInterior acoustical design is a relatively new profession, yet the acoustics was one of the human concerns throughout history. By primitiveness, humans from the prehistorical dates, automatically, used their instinct to adjust their interaction fields in a way to reach the best paths of sound communications.
\nLindsay’s wheel of acoustics shows the selective area of the architectural interior design, but the related fields that share in some range their outcomes and make benefits to the interiors are many (highlighted in the wheel (Figure 1)).
\nInterior acoustical design is a field where mixes of discipline contribute to the sound excellence for the benefits of the users. Interior acoustical design involves the basics of physics as science relates the reflection, absorption data, the sound formula regarding the reverberation time, the physical measurements of the materials based on the coefficient of absorption (alpha ᾱ), the space volume, and the sound frequencies. All these inputs enabled the creation of the new interior acoustical wheel (Figure 15) [12].
\nInterior acoustical design wheel.
Interior acoustical design implicates in parallel the artistic perception of the material selection, the material patterning, and the material color schemes. Therefore, the interior acoustical design is deliberated, as science and art, through the following points:
The
The
The interior acoustical space needs hard efforts to prevent the weakness of the
The interior acoustical design—or
The interior acoustical material classifications are reflectors, absorbers, and diffusers.
Isolation materials are within the architect responsibility, yet the interior designer could refer to them when needed.
The
The
The interior acoustical design is a combination of science, architecture, psychology, and design that contribute in interlacing paths to reach the sound excellence for the wellness of the human being.
Reflection position formulas [
Alpha chart to standard frequencies.
Sound level chart to the normal daily life, where the hearing damages are indicated.
Reverberation time (RT) measured in seconds for specific functions.
The acoustics from interior designer perspective reach the end. The following indices could conclude the main features of discussion, already detailed within this chapter:
Acoustics dates from the prehistorical eras, where the human considered it a vital component of the daily life, to sustain the communication.
The uses of the architectural layout and the interior components were the tools to apply a good acoustical environment, using the science, within the historical periods.
Interior acoustic design needs a clear classification of functional necessities to reach a successful synthesis that suits the end users of the spaces.
The interior acoustical success relies on the quality and the locus of the materials used within the space upon stricken regulations.
The material quality determines either it is a reflector, absorber, or diffuser.
Reflectors are hard, rigid, and thick materials.
Absorbers are soft and porous, and, usually, they are in the form of “sandwich panels.”
Diffusers are similar to the reflectors but never in plane surfaces, only if full of curves or angles.
The coefficient of absorption (alpha ᾱ) factor permits to apportion the material scheme within the interior. Alpha differs upon the level frequencies. When overall alpha magnitude reaches the round of “1,” it means the material is absorber, and when reaching the round of “zero,” it means the material is reflector.
Sound systems are used for only archetypical purposes and should follow the confine regulations.
Soundscape is an element to consider effectively, as it affects the human psychological within the interior layout.
I would like to express my gratitude to my professor, my mentor, and my father Professor Sami Abdelaziz Mahmoud, who was the main reason of my professional life, especially in the field of lighting and acoustics.
\n\n are hard solid materials to reflect the sound waves are soft porous materials to absorb the exceed sound are hard sold materials full of edges to scatter the sound decibel: measurement unit of the sound level. “deci” logarithm unit and “Bel” in honoring Alexander Graham Bel, the founder of the sound science Reverberation time is the lifetime of the sound until it becomes mute and measured by seconds hertz, the measurement unit of the sound frequencies the part in any loudspeaker responsible of the low frequencies the part in any loudspeaker responsible of the high frequencies
Since the Industrial Revolution in the eighteenth century, fossil fuels have been used as an energy source, contributing to increase the concentration of CO2 (carbon dioxide) in the atmosphere [1]. An increasing global concentration of CO2 in the atmosphere, from 290.0 parts per million (ppm) in 1870 [2] to 414.0 ppm in 2019 [3], occurred during the period which was marked by the Second and Third Industrial Revolution. This period is characterized by a significant increase in the use of fossil fuels as an energy source. The increased concentration of CO2 in the atmosphere results in temperature rise. The increase in the temperature is the major cause for all other changes on the earth’s climate. The rise in temperatures is causing warming of oceans, melting of ice mass, and increase in evaporation. Due to this increase in CO2 emissions and its consequences, the traditional concept of global development incorporated the environmental development. This incorporation resulted a broader concept referred to as sustainable development, which is based on the inseparability of economic, social, and environmental development [1]. Therefore, nowadays, integrated renewable energy system-based power generation has enormous growth and enhanced technological development due to increasing worldwide electricity demand, environmental concerns, and financial aspects [4].
In this context, renewable energy is at the center of the transition to a less carbon-intensive and more sustainable energy system. Renewable energy has grown rapidly in recent years, accompanied by sharp cost reductions for solar photovoltaics and wind power in particular [5]. Wind energy, a sustainable and a domestic source of energy that can reduce our dependency on fossil fuels, has developed rapidly in recent years. It’s mature technology and comparatively low cost make it promising as an important energy source in the next decades [6]. The electricity sector remains the brightest spot for renewables energy with the exponential growth of wind power in recent years in the world [7]. Figure 1 shows the global cumulative installed wind capacity 2001–2017 (adapted from [8]).
Wind power global capacity 2001–2017 (adapted from [
Brazil is a large country with regard to its sizable power system and continental distances, both in terms of grid extension and generating capacity. A prominent feature of its power system is the significance of its hydropower [9], which accounts for 59.90% [10] of the generation in the interconnected system. By the end of 2018, there were a total of 583 plants/wind farms and 14.71 GW of installed capacity, a 15.19% growth compared to December 2017, when the installed capacity was 12.77 GW. With an additional 1.94 GW, wind power now makes up 9.0% of the nation’s power matrix, which also shows the percent contribution from all sources of energy to the electric power grid at the end of 2018. It is important to remember that at the end of 2017, wind power accounted for 8.10% of the energy generated [10].
Uruguay surprisingly obtains 94.0% of its electricity from renewable sources [11]. In addition to old hydropower plants, large investments in solar, wind, and biomass have increased the proportion of these sources to 55.0% of the total energy (see that the global average is 12.0% and the European average is approximately 20.0%). In this way, wind power has attracted attention, and various wind farms have been constructed in Uruguay to harness wind energy. Among the countries of the world, Uruguay ranks fourth in the generation of wind energy, in accordance with [12]. Additionally, Uruguay and Brazil have good relationships, which contribute to its excellent growth with regard to wind and solar energy [13].
Regarding wind power, the variability of wind speed and wind direction throughout the day makes it difficult to decide whether to drive wind turbines, because wind exhibits temporal variations of several orders of magnitude, e.g., short-duration variations (bursts), hourly variations (owing to land and sea breezes), daily variations (owing to the local microclimate), seasonal variations, and annual variations (owing to climatic changes) [13]. The spatial variation of wind energy is also very large. The soil roughness and topography significantly influence the distribution and velocity of winds. Large fluctuations in wind speed make forecasting the power generated by wind turbines difficult; not to mention that economic losses occur if these turbines are subjected to unfavorable weather conditions [14].
Consequently, it is necessary to develop reliable tools to wind speed forecasting, even in the short-range. The interest in applications of mathematical modeling and numerical simulation of the atmosphere for the estimation of wind potential is increasing and driving a significant market. The use of computational models can help both the identification of locations with high wind potential and, when used operationally in daily integrations, in the short-term energy generation forecast [15]. The mainstream models used by scientific researchers can be divided into several categories [13, 16]: physical forecasting models, conventional statistical forecasting models, artificial intelligence forecasting models, statistical machine learning models, fuzzy logic-based models, spatial-correlation forecasting models, and hybrid models.
Computational models can be useful for the identification of locations with high wind potential and, when used operationally in daily integrations, short-term energy generation forecasting [13, 15]. In [13, 17, 18], among others, they obtained good results with small error via mathematical modeling and numerical simulation for short-term prediction using computational intelligence techniques, especially multilayer perceptron neural networks with feed-forward and back-propagation training algorithms. Although the previously cited authors demonstrated the applicability of artificial neural networks (ANN) in the next-step prediction of wind speed, none of them compared the performance of the results of wind speed forecasting 6 h ahead between Colonia Eulacio, Soriano Department, Uruguay (humid subtropical climate region), and Mucuri city, Bahia, Brazil (humid tropical climate region), using meteorological data collected by anemometers and not climatic data from global circulation models shown in [19].
This chapter presents two case studies about short-term wind speed forecasting in Brazil and Uruguay. The chapter is organized as follows. In part 2, the air and the wind power are briefly described. Part 3 describes the materials and methods, computational intelligence, and nowcasting. In part 4, the case studies are proposed. Conclusions are proposed in part 5.
The air in motion—what we commonly call wind—is invisible, yet we see evidence of it nearly everywhere we look. It transports heat, moisture, dust, insects, bacteria, and pollen from one area to another [20]. Inserted in this context, [21] explain that the winds are generated by pressure differences that arise because of unequal heating of the earth’s surface. The earth’s winds blow in an unending attempt to balance these surface temperature differences. As the zone of maximum solar heating migrates with the seasons—moving northward during the Northern Hemisphere summer and southward as winter approaches—the wind patterns that make up the general circulation also migrate latitudinally. Airflow (or wind) can be divided into three broad categories: waves, turbulence, and mean wind. Each can exist in the boundary layer, where transport of quantities such as moisture, heat, momentum, and pollutants is dominated in the vertical by turbulence and horizontal by the mean wind [22]. Each can exist in the presence of any of the others or separately.
The earth’s highly integrated wind system can be thought of as a series of deep rivers of air that encircle the planet. Embedded in the main currents are vortices of various sizes, including hurricanes, tornadoes, and midlatitude cyclones. Like eddies in a stream, these rotating wind systems develop and die out with somewhat predictable regularity. In general, the smallest eddies, such as dust devils, last only a few minutes, whereas larger and more complex systems, such as midlatitude cyclones and hurricanes, may survive for several days [21]. The scales of atmospheric motion shown in Table 1 illustrate the three major categories of atmospheric circulation: microscale, mesoscale, and macroscale (synoptic scale and global scale).
Scale | Typical size | Phenomenon | Life span |
---|---|---|---|
Microscale | 0–1.0 km | Small turbulent eddies, thunderstorms | Seconds to minutes |
Mesoscale | 1.0–100 km | Tornadoes, waterspouts, dust devils, land/sea breeze, mountain/valley breeze | Minutes to hours/days |
Synoptic scale | 100–5000 km | Hurricanes, tropical storms | Days to weeks |
Global scale | 1000–40,000 km | Longwaves in the westerlies, trade wind | Weeks to years |
In short, wind is the movement of air from an area of high pressure to an area of low pressure. In fact, wind exists because the sun unevenly heats the surface of the earth. As hot air rises, cooler air moves in to fill the void. As long as the sun shines, the wind will blow. And wind has long served as a power source to humans [23]. Wind spins the blades, which turn a shaft connected to a generator that produces electricity, in other words, wind turbines convert kinetic energy contained in the wind first into mechanical and then into electrical energy [24]. Wind is a clean source of renewable energy that produces no air or water pollution. And since the wind is free, operational costs are nearly zero once a turbine is erected. Mass production and technology advances are making turbines cheaper, and many governments offer tax incentives to spur wind-energy development [23].
Nowadays, wind turbine technology is considered matured, and the costs of wind energy are low [6]. Industry experts predict that if this pace of growth continues, by 2050 one third of the world’s electricity needs will be fulfilled by wind power [23]. Though wind power has performed well in recent years, it also creates a strong environmental impact, such as visual impact, climatic impact, and noise. Although these impacts seem minor when compared with nonrenewable energy, its effect on humans should not be overlooked, due to its potential great development in usage. In short, with proper and supportive policies toward wind power and a good understanding of its environmental impact, wind energy can be a clean and sustainable source of energy that can successfully replace fossil fuels [6].
In this chapter, we use computational intelligence by artificial neural networks for the next-step prediction of one climatic variable: wind speed. ANN was trained to perform the forecasting of 1 h ahead, and then, using it, the trained network was applied to recursively infer the forecasting for the next 6 h of the wind speed (nowcasting), following the methodology explained in [13]. The activation functions that define the outputs of the neurons in terms of their activity levels, inserted in this simulation, were the sigmoidal function in the form of the hyperbolic tangent function (characterized as continuous, increasing, differentiable, and nonlinear) for hidden layers and linear function to the output layer.
To train the RNN and validate the technique, anemometer data (average hourly values of wind speed, wind direction, and temperature) for 1 year (August 08, 2014, and August 07, 2015) are collected by one tower with anemometer installed at height of 101.8 m to Colonia Eulacio (Uruguay), and data (average hourly values of wind speed, wind direction, temperature, humidity, and pressure) for 1 month (November 30, 2015, until December 31, 2015) are collected by one tower with anemometers installed at height of 100.0 m to Mucuri (Brazil), using the same criteria as in [13], namely, 70% for training/validating data and 30% for simulation. The reason for choosing these periods is these are the months with the totality of data available for the realization of this study.
The Mucuri city (Bahia, Brazil) is located at an altitude of 7.0 m in relation to the sea level, and it has a territorial area of 1775 km2, approximately. Mucuri’s anemometer tower is located in a coastal plain, at a distance of 340.0 m from the sea, with latitude 18°1′31.52″ S and longitude 39°30′51.69″ W (Figure 2).
Location of the Mucuri Tower in Bahia, Brazil.
As for Colonia Eulacio Tower in Uruguay, according to datum WGS84, it is located at 33o16′ S, 57o31′ W [25]. The altitude of the installation location (see Figure 3) is approximately 100.0 m, and the location is surrounded by fields with plains; thus, it is characterized by noncomplex terrain. The station is owned by the Administración Nacional de Usinas y Transmissiones Eléctricas (UTE), which is a state-owned company in Uruguay that is responsible for the generation, distribution, and commercialization of electrical energy in the country, as cited in [13].
Location of the Colonia Eulacio Tower in Soriano Department, Uruguay [
The insertion of meteorological parameters as input data contributes to efficient training of the ANN. Seven different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results (MAE, MSE, and RMSE) are evaluated to select the configuration that best predicts the real data. The proposed ANN configurations to be analyzed are the following ones. For Mucuri (Brazil) the best ANN configuration was Configuration 1 and for Colonia Eulacio (Uruguay) was the Configuration 4.
Configuration 1: three layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), nine hidden neurons, and one output node
Configuration 2: three layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), six hidden neurons, and one output node
Configuration 3: three layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), three hidden neurons, and one output node
Configuration 4: three layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), one hidden neuron, and one output node
Configuration 5: four layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), nine hidden neurons (first hidden layer) and six hidden neurons (second hidden layer), and one output node
Configuration 6: four layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), six hidden neurons (first hidden layer) and three hidden neurons (second hidden layer), and one output node
Configuration 7: four layers, nine input nodes (site, Brazil) or seven input nodes (site, Uruguay), one hidden neuron (first hidden layer) and one hidden neuron (second hidden layer), and one output node
For statistical analysis of wind speed prediction results at the above sites, the following statistical indicators were applied: Pearson’s correlation coefficient (r or Pearson’s r), coefficient of determination (R2 or R-squared), mean absolute percentage error (MAPE), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). Pearson’s correlation coefficient ranges from −1.0 to 1.0. Values close to 0.0 are adequate for the MAE, MSE, and RMSE, values close to 0.0% are adequate for the MAPE, and values close to 1.0 are adequate for the R-squared. The software used to program and perform this computational procedure was MATLAB version 7.10.0 (2010) (personal computer, 8 GB RAM), as the methodology applied in [13].
Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Over the last few years, there has been an explosion of research on machine learning and deep learning. Nowadays, deep learning has become the core method for artificial intelligence (AI) [26]. AI is one of the newest fields in science and engineering. AI currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, diagnosing diseases, and predicting the conditions of the atmosphere for a given location and time. AI is relevant to any intellectual task; it is truly a universal field [27]. In fact, some of the most successful AI systems are based on CI.
In the early days of artificial intelligence, the field rapidly tackled and solved problems that are intellectually difficult for human beings but relatively straightforward for computers—problems that can be described by a list of formal, mathematical rules [28]. Many artificial intelligence tasks can be solved by designing the right set of features to extract for that task, then providing these features to a simple machine learning algorithm. The most widely used artificial neuron model is the perceptron proposed in [29, 30]. This model defines a neuron composed of inputs, a summation and an activation function. The value of each input is multiplied by a weight, and the weighted values of the inputs are summed to yield the result of the sum which is used as the input of the activation function. To teach (train) the neuron, the weights are modified so that the output obtained corresponds to the desired value [30].
The multilayer perceptron (MLP) consists of a system of simple interconnected neurons, or nodes, which is a model representing a nonlinear mapping between an input vector and an output vector. The architecture of a MLP is variable but in general will consist of several layers of neurons. The input layer plays no computational role but merely serves to pass the input vector to the network. The terms input and output vectors refer to the inputs and outputs of the MLP and can be represented as single vectors [31]. Moreover, in relation to recurrent neural network (RNN), the definition is that they are powerful sequence-processing models that are equipped with a memory from recurrent feedback connections. One of the current main challenges of RNN is to dynamically adapt to multiple temporal resolutions and scales in order to learn hierarchical representations in time. Since they operate in discrete time steps and update at every time step, it is generally difficult to learn temporal features that have a significantly different resolution than their input frequency [32].
Predicting the short-term power output of a wind turbine (wind energy converter) is an important task for the efficient management of smart grids. Short-term forecasting at the minute scale also is known as nowcasting. By definition nowcasting refers to short lead time weather forecasts, the US National Weather Service specifies zero to 3 h, though forecasts up to 6 h may be called nowcasts by some agencies [33]. Nowcasting is critical when managing operations of the smart grid, such as system integration, ensuring power continuity and managing ramp rates [34]. In this chapter, nowcasting refers to short-term wind speed forecasting 6 h ahead. In [35] they described and evaluated a proposal for nowcasting wind speed for wind farm locations from historical time series, based on the method of regression by support vectors (in short, nowcasting of wind speed using support vector regression: experiments with time series from Gran Canaria).
The figures below show the time series used in the models which consist of 744 h for Mucuri in Brazil (Figure 4) and 8760 h for Colonia Eulacio in Uruguay (Figure 5), corresponding to hourly mean wind speed data. As can be observed in the figures, there is noticeable data randomness, and it is difficult to find a series tendency or seasonality.
The experimental wind speed time series—Mucuri (Brazil).
The experimental wind speed time series—Colonia Eulacio (Uruguay).
In this sense, a descriptive statistic regarding wind speed at different sites is shown in Table 2. It can be noted that wind speed data measured at Colonia Eulacio has a higher variability.
Site | Arithmetic mean of wind speed [m/s] | Variance [m2/s2] | Standard deviation [m/s] |
---|---|---|---|
Mucuri | 7.91 | 8.53 | 2.92 |
Colonia Eulacio | 7.21 | 9.02 | 3.00 |
Statistics (Mucuri and Colonia Eulacio).
Tables 3–6 show the evaluation metrics of the prediction results obtained by the proposed model. Table 3 presents simulation results referring to errors in wind speed forecasting 1, 3, and 6 h ahead for Mucuri using the RNN model. In Table 4, we present the results for regression for Mucuri. In Table 5, we can see errors in wind speed forecasting 1, 3, and 6 h ahead for Colonia Eulacio using the RNN model. Table 6 shows the regression results to Colonia Eulacio. The percentage of the data of a factor of two is a fraction of data [%] for 0.5 ≤ wind predicted/wind anemometer ≤ 2.0. Table 4 shows that the percentage of the data of a factor of two [%] to Mucuri is bigger than the percentage of the data of a factor of two [%] to Colonia Eulacio (see Table 6).
1 | 0.839 | 1.111 | 1.054 | 11.07 |
3 | 1.385 | 3.154 | 1.775 | 17.63 |
6 | 1.779 | 5.108 | 2.260 | 21.26 |
Simulation results (errors): wind speed forecasting 1, 3, and 6 h ahead for Mucuri using the RNN model.
Prediction horizon [h] | Pearson correlation coefficient | Coefficient R2 | Percentage of the data of a factor of two [%] |
---|---|---|---|
1 | 0.940 | 0.885 | 99.48 |
3 | 0.850 | 0.723 | 98.95 |
6 | 0.742 | 0.550 | 98.94 |
Simulation results (regression): wind speed forecasting 1, 3, and 6 h ahead for Mucuri using the RNN model.
Prediction horizon [h] | MAE | MSE | RMSE | MAPE [%] |
---|---|---|---|---|
1 | 0.895 | 1.408 | 1.187 | 16.08 |
3 | 1.687 | 4.710 | 2.170 | 30.65 |
6 | 2.266 | 8.051 | 2.837 | 39.86 |
Simulation results (errors): wind speed forecasting 1, 3, and 6 h ahead for Colonia Eulacio using the RNN model.
Prediction horizon [h] | Pearson correlation coefficient | Coefficient R2 | Percentage of the data of a factor of two [%] |
---|---|---|---|
1 | 0.922 | 0.849 | 98.44 |
3 | 0.729 | 0.531 | 93.60 |
6 | 0.543 | 0.295 | 88.52 |
Simulation results (regression): wind speed forecasting 1, 3, and 6 h ahead for Colonia Eulacio using the RNN model.
Figures 6–8 show the results of six-step predictions of the wind speed series to Colonia Eulacio and to Mucuri. In Figure 6, we observe wind speed forecasting results of the model with RNN in six-step ahead for Colonia Eulacio, Uruguay. Figure 7 presents wind speed forecasting 6 h ahead in a period of 744 ho of anemometric tower measurements. In Figure 8, we can see wind speed forecasting results of the model with RNN in six-step ahead for Mucuri, Brazil.
Wind speed forecasting results of the model with RNN in six-step ahead (Colonia Eulacio, Uruguay).
Wind speed forecasting results of the model with RNN in six-step ahead (Colonia Eulacio, Uruguay), April 20, 2015, at 13:00 to April 28, 2015, at 14:00.
Wind speed forecasting results of the model with RNN in six-step ahead (Mucuri, Brazil).
Figures 9 and 10 show the multistep root mean square error (RMSE) evaluation of RNN and MLP for Mucuri, Brazil, and Colonia Eulacio, Uruguay. It is observed in Figure 9 that as the prediction horizon increases, RNN were more efficient when compared to those employed in the study which applies MLP referenced by [36]. These results indicate that if we want a higher accuracy in the result to Mucuri, we must use a recurrent neural network. A recurrent neural network allows self-loops and backward connections between all neurons in the network. That enables the networks to do temporal processing and learn sequences, e.g., temporal association/prediction.
The multistep root mean square error evaluation of different architecture for Mucuri, Brazil.
The multistep root mean square error evaluation of different architecture for Colonia Eulacio, Uruguay.
Unlike the previous comparison, for Colonia Eulacio’s anemometers data, the study which applies MLP referenced by [13] was more appropriate (Figure 10) than this study that applies RNN.
The multistep Pearson correlation coefficient evaluation of different architecture and site is shown in Figure 11.
The multistep Pearson correlation coefficient evaluation of different architecture and site.
The computational cost employed to simulate wind speed prediction through RNN for Colonia Eulacio is not viable when compared to the application of MLP. In contrast, for Mucuri, it is considerably more viable to apply RNN, as can be seen from the figure above. Lastly, nowadays, adopting renewable energy has become a national energy policy for many countries due to concerns with pollution from fossil fuel consumption and climate change. Regarding wind energy, the accurate prediction of wind is crucial in managing the power load. Thus, this work presented the short-term wind speed forecasting for two representative sites in South America, Brazil, and Uruguay, which are the most important countries in terms of renewable energy production in Latin America.
Each scientific study on wind speed prediction has its own characteristics, such as the height of the anemometer that records atmospheric data (e.g., wind speed, direction, temperature, humidity, and atmospheric pressure) and the time series of this atmospheric data. These data are applied to train and test the efficiency of the ANN. On the accuracy of the use of ANN in the estimation of short-term wind speed and wind power forecasting, we can mention these earlier studies (see Table 7). The MAE average value for 1 h ahead was 0.847 m/s and for 3 h ahead was 1.420 m/s.
Prediction horizon [h] | Mucuri: this study | Colonia Eulacio: this study | Reference [13] | Reference [36] | Reference [37] | Reference [38] |
---|---|---|---|---|---|---|
Mean absolute error | ||||||
1 | 0.839 | 0.895 | 0.892 | 0.720 | 1.050 | 0.684 |
3 | 1.385 | 1.687 | 1.678 | 1.370 | 1.230 | 1.168 |
Accuracy of the use of ANN in the estimation of wind speed and wind power.
Other scientific research on wind speed has been developed. We can cite the works in [39, 40]. In [39] they analyzed the time series of wind speeds in Mucuri, Mucugê, and Esplanada, cities of the state of Bahia, and the Abrolhos Archipelago, Brazil, through the use of the detrended fluctuation analysis technique to verify the existence of long-range correlations and associated power laws. Already [40] describes a short-term wind energy forecasting tool based on a run set forecasting system of the WRF-GFS model that has been operationally implemented in the electricity system in Uruguay with estimates for Brazil wind energy production.
The present chapter aimed to define the most efficient RNN configuration to predict the wind speed for 1 h and, after that, to apply it for 6 h ahead, using as reference observational data collected from two anemometric towers, with anemometers installed at 100.0 and 101.8 m height, located, respectively, in a tropical region in Mucuri, Bahia, northeastern Brazil, and in a subtropical region in Colonia Eulacio, Soriano Department, Uruguay. It has been shown graphically and verified through numerical simulations that the RNN was better than MLP in Mucuri and worst in Colonia Eulacio.
In the light of the statistical results recorded in this work, the application of computational intelligence is a viable alternative for the predictability of wind speed and, in this way, wind power generation, mainly due to the low computational cost; however, one must choose the ANN architecture that best suits the project, as well as quantitatively and qualitatively analyzes the available data that will feed the network, since these variables directly impact the results of the forecast.
The results of the short-term wind speed forecasting showed good accuracy at all the anemometer heights tested. Therefore, the proposed short-term wind speed forecasting method is an important scientific contribution for reliable large-scale wind power forecasting and integration in tropical and subtropical regions, like in Brazil and Uruguay.
The suggestion for improving the accuracy of ANN for higher lead time is wavelet packet decomposition because the empirical wavelet transform can effectively identify and extract a finite number of intrinsic modes of a wind speed time series and thus improving the accuracy of the supervised machine learning; other suggestion is to apply the wind speed x-axis component and wind speed y-axis component ANN’s input.
We can suggest as future work to use the Mucuri, Colonia Eulacio, and other observational data collected in different heights in Brazil and Uruguay to perform the prediction of the wind speed more accurately in the short-term and in the medium-term using computational intelligence by long short-term memory (LSTM) and gated recurrent unit (GRU) and to compare these results with the output produced by numerical and meteorological modeling using the weather research and forecasting (WRF) model, for example. Wind ramp and greater forecasting horizons are also a great subject of research.
We thank SENAI CIMATEC and FAPESB for their computational and financial support, as well as UTE and UFES.
The authors declare no conflict of interest.
We thank IntechOpen, the world’s leading publisher of Open Access books, for this opportunity.
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