A hybrid ensembled double-input-fuzzy-modules based precise prediction of PV power generation. (July 2022) - Zhang, Hualu; Shi, Jie; Zhang, Chunping
Abstract: As one of the most widespread renewable energy technologies, photovoltaic power generation system provides great environmental and economic benefits. Achieving precise prediction for the PV power generation will greatly improve the quality of electric energy and enhance the stability of power system operation. Inspired by the powerful ability of fuzzy logic to deal with uncertainty and the superiority of machine learning to handle the time series prediction, a hybrid ensembled model consisted of the Double-Input-Fuzzy-Modules (DIFM) and Extreme Learning Machine is proposed in this paper. Firstly, the PV power generation data is taken as the input of each DIFM in order to efficiently handle the uncertainties. Then, the outputs of each DIFM are used as the input of ELM. Moreover, the least square estimation is applied to train the parameters of the hybrid ensembled model to further enhance the predict precision. Finally, the proposed hybrid ensembled model is applied to achieve a real-world PV power generation forecasting. The cases study and comparisons results indicate that the proposed hybrid ensembled model outperforms other methods, in terms of the mean absolute error, the root means square error and the coefficient of determination.