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Prediction techniques for renewable energy generation and load demand forecasting

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prediction techniques for renewable energy generation and load demand forecastingedited by Anuradha Tomar, Prerna Gaur, Xiaolong Jin.
other author:	Tomar, Anuradha.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xii, 198 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electric power productionForecasting.
Online resource:	https://doi.org/10.1007/978-981-19-6490-9
ISBN:	9789811964909$q(electronic bk.)

Prediction techniques for renewable energy generation and load demand forecasting
Prediction techniques for renewable energy generation and load demand forecasting[electronic resource] /edited by Anuradha Tomar, Prerna Gaur, Xiaolong Jin. - Singapore :Springer Nature Singapore :2023. - xii, 198 p. :ill., digital ;24 cm. - Lecture notes in electrical engineering,v. 9561876-1119 ;. - Lecture notes in electrical engineering ;v.132..

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.

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.

ISBN: 9789811964909$q(electronic bk.)

Standard No.: 10.1007/978-981-19-6490-9doiSubjects--Topical Terms:

946538
Electric power production
--Forecasting.

LC Class. No.: TK1001 / .P74 2023

Dewey Class. No.: 621.31210285

Prediction techniques for renewable energy generation and load demand forecasting
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505 0 $a 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.
520 $a 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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