TY  - GEN
AB  - 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.
AU  - Tomar, Anuradha,
AU  - Gaur, Prerna,
AU  - Jin, Xiaolong
CN  - TK1001
DO  - 10.1007/978-981-19-6490-9
DO  - doi
ID  - 1454252
KW  - Electric power production
KW  - Electric power production
KW  - Renewable energy sources
KW  - Renewable energy sources
KW  - Electric power systems
KW  - Electric power systems
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-6490-9
N2  - 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.
SN  - 9789811964909
SN  - 9811964904
T1  - Prediction techniques for renewable energy generation and load demand forecasting /
TI  - Prediction techniques for renewable energy generation and load demand forecasting /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-6490-9
VL  - volume 956
ER  -