Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting (eBook, PDF)

Redaktion: Tomar, Anuradha; Jin, Xiaolong; Gaur, Prerna

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 198
• Erscheinungstermin: 20. Januar 2023
• Englisch
• ISBN-13: 9789811964909
• Artikelnr.: 67320373

Autorenporträt

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. Prof. Prerna Gaur has completed her B.Tech. in Electrical Engineering (1988), M.Tech (1996) and Ph.D. (2009), Presently, Director, NSUT, East Campus. Professor & founder Head in Instrumentation and Control and Electrical Engineering Department in NSUT. Six years of Industry experience and 28 years of Teaching. H index-19 and i10 index -42. She is Director & Member Secretary, Business Incubator of NSUT and NBA Co-ordinator of NSUT. Has organized IEEE international conference DELCON2022, INDICON2020 and IICPE-2010 and at NSUT. She is actively associated with IEEE (Senior Member), ISTE (Life Member), IETE Fellow and IE (Fellow). Treasurer, IEEE India Council from Jan 2021. Chair, IEEE Delhi Section 2019-20.  Outstanding Branch Counsellor and Advisor Award 2021, IEEE Member of Geographic Activities. Outstanding Volunteer Award, from IEEE India Council, 2019, Women of the Decade in Academia, 2018. Maulana Abul Kalam Azad Excellence award in Education-2015. IEEE PES Outstanding Chapter Engineer Award 2015 from IEEE Delhi Section, Outstanding Chapter award from IEEE PELS, NJ, USA 2013.Outstanding Branch Counselor Award from Region 10 (Asia Pacific Region) in 2012 and from IEEE USA in 2009.   Xiaolong Jin received the B.S., M.S. and Ph.D. degrees in electrical engineering from Tianjin University, China, in 2012, 2015 and 2018, respectively. He is currently Postdoc Researcher with Technical University of Denmark. From 2017 to 2019, he was a joint Ph.D. student with the School of Engineering, Cardiff University, Cardiff, UK. His research interests include energy management of multi-energy buildings and their integrations with integrated energy systems and the energy and flexibility markets solutions.

Inhaltsangabe

Artificial Intelligence for renewable energy prediction.- Solar Power Forecasting in Photovoltaic Cells using Machine Learning.- Hybrid techniques for renewable energy prediction.- A Deep Learning-based Islanding Detection Approach by Considering the Load Demand of DGs under Different Grid Conditions.- Quantitative forecasting techniques-Comparison of PV power production estimation methods under non-homogenous temperature distribution for CPVT systems.- Renewable Energy Predictions: Worldwide Research Trends and Future perspective.- Models in Load forecasting.- Machine Learning techniques for Load forecasting.- Hybrid techniques for Load forecasting-Time Load Forecasting: A smarter expertise through modern methods.- Deep Learning techniques for Load forecasting.

Rezensionen

"The book is an authoritative guide, making an invaluable contribution to the literature on renewable energy forecasting. ... we highly recommend this book for its comprehensive coverage of the subject, insightful perspectives, practical examples, and accessible writing style. The authors should be commended for this stellar contribution to the literature on renewable energy prediction, which will undoubtedly become a go-to resource for professionals, researchers, students, and policymakers alike." (Dani Pasaribu, Alrend Roy Peterson Kaputing , and Delvianus Kaesmentan, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 29 (1-2), 2024)