Prediction techniques for renewable energy generation and load demand forecasting / Anuradha Tomar, Prerna Gaur, Xiaolong Jin, editors.
Tomar, Anuradha, editor.; Gaur, Prerna, editor.; Jin, Xiaolong (Electrical engineer), editor.
2023
TK1001 .P74 2023
Available Online

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Title Prediction techniques for renewable energy generation and load demand forecasting / Anuradha Tomar, Prerna Gaur, Xiaolong Jin, editors.
ISBN 9789811964909 electronic book
9811964904 electronic book
9789811964893
9811964890
DOI https://doi.org/10.1007/978-981-19-6490-9
Published Singapore : Springer, [2023]
Language English
Description 1 online resource (204 pages) : illustrations (black and white, and color).
Item Number 10.1007/978-981-19-6490-9 doi
Call Number TK1001 .P74 2023
Dewey Decimal Classification 621.31210285
Summary This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on March 08, 2023).
Added Author Tomar, Anuradha, editor.
Gaur, Prerna, editor.
Jin, Xiaolong (Electrical engineer), editor.
Series Lecture notes in electrical engineering ; v. 956.
Available in Other Form Prediction techniques for renewable energy generation and load demand forecasting.
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