A hybrid ensembled double-input-fuzzy-modules based precise prediction of PV power generation

BackgroundAs one of the most widespread renewable energy technologies, photovoltaic power generation system provides great environmental and economic benefits. Uncertain output of the photovoltaic power generation system gets a lot of concern, due to its randomicity and intermittence features. 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. ResultsThe case study and comparisons results indicate that the proposed hybrid ensembled model outperforms other methods, including the adaptive network-based fuzzy inference system, the single-input-rule-modules connected fuzzy model, the support vector regression and the multiple linear regression, in terms of the mean absolute error, the root means square error, the mean absolute error percentage and the mean relative error. ConclusionsThis study fully took the advantages of fuzzy logic to deal with uncertainty and the superiority of machine learning to handle the time series prediction, then combined the DIFM and Extreme Learning Machine to propose a hybrid ensembled DIFM based PV power generation prediction model. Experimental results demonstrated the superiorities of the proposed model over the other comparisons, in both prediction accuracy and calculation speed.