To improve the accuracy of power generation prediction in high penetration photovoltaic microgrids, this paper proposes an improved Markov chain displacement time series prediction method （MFM） to optimize the problems of insufficient feature extraction and inaccurate prediction in the conjugate gradient method （CG） - deep belief network （DBN） combined prediction model （CG-DBN）. Firstly, using Pearson correlation coefficient to analyze the influencing factors of high penetration photovoltaic microgrid power generation; Secondly, taking advantage of the inefficiency of Markov chain displacement time series prediction method, the residual correction process is applied to the CG-DBN prediction model to construct a short-term prediction model for the power generation of MFM-CG-DBN high penetration photovoltaic microgrids; Finally, the MFM-CG-DBN short-term prediction model is used to simulate the power generation data of high penetration photovoltaic microgrids under three types of weather conditions： sunny, cloudy, and rainy. The simulation results show that the proposed short-term prediction model has higher prediction accuracy than the traditional CG-DBN short-term prediction model, and can meet the demand for high penetration photovoltaic microgrid power generation prediction.

Markov chains  /  microgrids  /  photovoltaic power  /  high permeability  /  MFM-CG-DBN