Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting
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Beschreibung
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.
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.
Provides an introduction to forecasting methods for renewable energy sources integrated with existing grid Includes artificial intelligence, machine learning, hybrid techniques, and other state-of-the-art techniques Helps readers in enhancing their knowledge in the field of power system, sustainable energy, and power engineering
Autor*in
Anuradha Tomar
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Artificial Intelligence Deep Learning Machine Learning Renewable Energy Predictions Load Forecasting Prediction Techniques Uncertainty Analysis
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“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)
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