Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting (eBook)
XII, 198 Seiten
Springer Nature Singapore (Verlag)
978-981-19-6490-9 (ISBN)
Dr. Anuradha Tomar has 12 years plus experience in research and academics. She is currently working as Assistant Professor in Instrumentation and Control Engineering Department of Netaji Subhas University of Technology, Delhi, India. Dr. Tomar has completed her postdoctoral research in Electrical Energy Systems Group, from Eindhoven University of Technology (TU/e), the Netherlands, and has successfully completed European Commission's Horizon 2020, UNITED GRID and UNICORN TKI Urban Research projects as a member. She has received her B.E. Degree in Electronics Instrumentation and Control with Honours in the year 2007 from University of Rajasthan, India. In the year 2009, she has completed her M.Tech. Degree with Honours in Power System from National Institute of Technology Hamirpur. She has received her Ph.D. in Electrical Engineering from Indian Institute of Technology Delhi (IITD). Dr. Anuradha Tomar has committed her research work efforts towards the development of sustainable, energy-efficient solutions for the empowerment of society, humankind. Her areas of research interest are operation and control of microgrids, photovoltaic systems, renewable energy-based rural electrification, congestion management in LV distribution systems, artificial intelligent and machine learning applications in power system, energy conservation and automation. She has authored or co-authored 69 research/review papers in various reputed international, national journals and conferences. She is Editor for books with international publications like Springer and Elsevier. Her research interests include photovoltaic systems, microgrids, energy conservation and automation. She has also filed seven Indian patents on her name. Dr. Tomar is Senior Member of IEEE and Life Member of ISTE, IETE, IEI and IAENG.
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.
Erscheint lt. Verlag | 20.1.2023 |
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Reihe/Serie | Lecture Notes in Electrical Engineering | Lecture Notes in Electrical Engineering |
Zusatzinfo | XII, 198 p. 68 illus., 54 illus. in color. |
Sprache | englisch |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Technik ► Elektrotechnik / Energietechnik | |
Schlagworte | Artificial Intelligence • Deep learning • load forecasting • machine learning • Prediction techniques • Renewable Energy Predictions • uncertainty analysis |
ISBN-10 | 981-19-6490-4 / 9811964904 |
ISBN-13 | 978-981-19-6490-9 / 9789811964909 |
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