國立高雄大學圖資館 |

登入

回首頁

Prediction techniques for renewable energy generation and load demand forecasting

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prediction techniques for renewable energy generation and load demand forecastingedited by Anuradha Tomar, Prerna Gaur, Xiaolong Jin.
其他作者:	Tomar, Anuradha.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xii, 198 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Electric power productionForecasting.
電子資源:	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
LDR:02712nmm a2200337 a 4500 001 640042
003 DE-He213
005 20230124134808.0
006 m d
007 cr nn 008maaau
008 231025s2023 si s 0 eng d
020 $a 9789811964909$q(electronic bk.)
020 $a 9789811964893$q(paper)
024 7 $a 10.1007/978-981-19-6490-9 $2 doi
035 $a 978-981-19-6490-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TK1001 $b .P74 2023
072 7 $a THR $2 bicssc
072 7 $a TEC031000 $2 bisacsh
072 7 $a THR $2 thema
082 0 4 $a 621.31210285 $2 23
090 $a TK1001 $b .P923 2023
245 0 0 $a Prediction techniques for renewable energy generation and load demand forecasting $h [electronic resource] / $c edited by Anuradha Tomar, Prerna Gaur, Xiaolong Jin.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xii, 198 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in electrical engineering, $x 1876-1119 ; $v v. 956
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.
650 0 $a Electric power production $x Forecasting. $3 946538
650 0 $a Electric power production $x Data processing. $3 238370
650 0 $a Renewable energy sources $x Forecasting. $3 946539
650 0 $a Renewable energy sources $x Data processing. $3 524598
650 0 $a Electric power systems $x Mathematical models. $3 238335
650 0 $a Electric power systems $x Load dispatching. $3 190512
650 1 4 $a Electrical Power Engineering. $3 919410
650 2 4 $a Renewable Energy. $3 918996
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Tomar, Anuradha. $3 841720
700 1 $a Gaur, Prerna. $3 946537
700 1 $a Jin, Xiaolong. $3 254740
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in electrical engineering ; $v v.132. $3 545003
856 4 0 $u https://doi.org/10.1007/978-981-19-6490-9
950 $a Energy (SpringerNature-40367)