A combined wind-PV power prediction method based on clustering prescreening

Abstract

Due to the volatility caused by the high uncertainty of renewable energy, the large-scale integration of renewable energy into the electric power grid brings huge challenges to the scheduling and operation of the electric power system. Effective power prediction of power source can not only relieve the pressure of power system peaking and frequency regulation, but also improve the accuracy of decision-making. So as to make the most of the information between wind and photovoltaic power source, this paper puts forward a wind-PV joint power forecasting model. First, the raw power data is pre-screened by clustering to select the closer connection between wind and PV power; next, the screened data are passed through the upper CNN structure respectively after pre-processed. Then, the outputs from CNN modules are fed together into the prediction layer, which include LSTM layers and fully connected layers. Finally, the proposed wind-PV joint prediction model is verified by simulation experiments.