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Decision and Control in Hybrid Wind Farms.
Book 217 of 378: Studies in Systems, Decision and ControlLanguage: English
Published by Singapore, Springer., 2020
ISBN 10: 9811502749 ISBN 13: 9789811502743
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
Association Member: GIAQ ILAB VDA
Seller rating 5 out of 5 stars
XXII, 140 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in Systems, Decision and Control, Volume 253. Sprache: Englisch.
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Condition: As New. Unread book in perfect condition.
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US$ 133.37
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Decision and Control in Hybrid Wind Farms (Studies in Systems, Decision and Control, 253)
Book 217 of 378: Studies in Systems, Decision and ControlSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Decision and Control in Hybrid Wind Farms (Studies in Systems, Decision and Control)
Book 217 of 378: Studies in Systems, Decision and ControlSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Decision and Control in Hybrid Wind Farms
Book 217 of 378: Studies in Systems, Decision and ControlSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
Language: English
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128213531 ISBN 13: 9780128213537
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketPaperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
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Condition: New. pp. XXII, 140 64 illus., 62 illus. in color. 2020th edition NO-PA16APR2015-KAP.
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Decision and Control in Hybrid Wind Farms
Book 217 of 378: Studies in Systems, Decision and ControlSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
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Condition: As New. Unread book in perfect condition.
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction (Volume 1) (Wind Energy Engineering, Volume 1)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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US$ 175.46
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Add to basketPaperback. Condition: Brand New. 164 pages. 9.25x6.10x0.39 inches. In Stock.
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Decision and Control in Hybrid Wind Farms (Studies in Systems, Decision and Control)
Book 217 of 378: Studies in Systems, Decision and ControlUS$ 177.72
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Add to basketHardcover. Condition: Brand New. 164 pages. 9.25x6.10x0.59 inches. In Stock.
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Decision and Control in Hybrid Wind Farms
Book 217 of 378: Studies in Systems, Decision and ControlLanguage: English
Published by Springer, Palgrave Macmillan, 2019
ISBN 10: 9811502749 ISBN 13: 9789811502743
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on two of the most important aspects of wind farm operation: decisions and control. The first part of the book deals with decision-making processes, and explains that hybrid wind farm operation is governed by a set of alternatives that the wind farm operator must choose from in order to achieve optimal delivery of wind power to the utility grid. This decision-making is accompanied by accurate forecasts of wind speed, which must be known beforehand. Errors in wind forecasting can be compensated for by pumping power from a reserve capacity to the grid using a battery energy storage system (BESS). Alternatives based on penalty cost are assessed using certain criteria, and MCDM methods are used to evaluate the best choice. Further, considering the randomness in the dynamic phenomenon in wind farms, a fuzzy MCDM approach is applied during the decision-making process to evaluate the best alternative for hybrid wind farm operation. Case studies from wind farms in theUSA are presented, together with numerical solutions to the problem. In turn, the second part deals with the control aspect, and especially with yaw angle control, which facilitates power maximization at wind farms. A novel transfer function-based methodology is presented that controls the wake center of the upstream turbine(s); lidar-based numerical simulation is carried out for wind farm layouts; and an adaptive control strategy is implemented to achieve the desired yaw angle for upstream turbines. The proposed methodology is tested for two wind farm layouts. Wake management is also implemented for hybrid wind farms where BESS life enhancement is studied. The effect of yaw angle on the operational cost of BESS is assessed, and case studies for wind farm datasets from the USA and Denmark are discussed. Overall, the book provides a comprehensive guide to decision and control aspects for hybrid wind farms, which are particularly important from an industrial standpoint.
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Taschenbuch. Condition: Neu. Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction | Harsh S. Dhiman (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Academic Press | EAN 9780128213537 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Condition: As New. Unread book in perfect condition.
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Decision and Control in Hybrid Wind Farms (Studies in Systems, Decision and Control)
Book 217 of 378: Studies in Systems, Decision and ControlUS$ 194.69
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Artificial Intelligence for Renewable Energy Systems
Book 8 of 16: Artificial Intelligence and Soft Computing for Industrial TransformationSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Artificial Intelligence for Renewable Energy Systems (Hardcover)
Book 8 of 16: Artificial Intelligence and Soft Computing for Industrial TransformationLanguage: English
Published by John Wiley & Sons Inc, New York, 2022
ISBN 10: 1119761697 ISBN 13: 9781119761693
Hardcover. Condition: new. Hardcover. ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in todays world, this book was designed to enhance the readers knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Artificial Intelligence for Renewable Energy Systems
Book 8 of 16: Artificial Intelligence and Soft Computing for Industrial TransformationSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Artificial Intelligence for Renewable Energy Systems (Hardcover)
Book 8 of 16: Artificial Intelligence and Soft Computing for Industrial TransformationLanguage: English
Published by John Wiley & Sons Inc, New York, 2022
ISBN 10: 1119761697 ISBN 13: 9781119761693
US$ 227.12
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Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: new. Hardcover. ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in todays world, this book was designed to enhance the readers knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
