Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study

• Published in: Applied soft computing Vol. 93; p. 106389
• Main Authors: Niu, Dongxiao, Wang, Keke, Sun, Lijie, Wu, Jing, Xu, Xiaomin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2020
• Subjects: Complementary ensemble empirical mode decomposition; Improved back propagation neural network; PV power generation forecasting; Random forest; Similar days discrimination; Random forest; Improved back propagation neural network; Similar days discrimination; PV power generation forecasting; Complementary ensemble empirical mode decomposition

To mitigate solar curtailment caused by large-scale development of photovoltaic (PV) power generation, accurate forecasting of PV power generation is important. A hybrid forecasting model was constructed that combines random forest (RF), improved grey ideal value approximation (IGIVA), complementary...