A novel photovoltaic power forecasting model based on echo state network

Abstract In this paper, a novel photovoltaic (PV) forecasting model based on multiple reservoirs echo state network (MR-ESN) is proposed to forecast the power output of PV generation system. Firstly, through the unsupervised learning algorithm of restricted Boltzmann machine, the relative feature of input information can be extracted. According to the forecasting performance evaluation criteria of PV forecasting model, principal component analysis is used to extract the main feature, such that the inputs of MR-ESN and the number of reservoirs of MR-ESN can be determined. Secondly, in order to improve the prediction accuracy, an improved parameter optimization method based on Davidon–Fletcher–Powell (DFP) quasi-Newton algorithm is given to optimize the reservoir parameters of MR-ESN. Thirdly, in order to guarantee that the MR-ESN can be stably applied for PV power forecasting, a sufficient condition of the transient stability of PV forecasting model is given. Finally, a PV power generation forecasting example shows that the proposed PV forecasting model can significantly improve the forecasting performance.

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