Forecast of PV power generation based on residual correction of Markov chain

With the increase of the capacity of photovoltaic (PV) systems, how to alleviate the problem caused by the random output power of PV system becomes significant. This paper uses Markov model to correct prediction results of power generation for grid-connected PV system based on gray neural network model. Using the hourly data of PV system output power under similar climate conditions, gray model of the output power is established in real-time. Then, the output of the gray model, the temperature, irradiance and measured values are used to build the prediction model with neural network, and the residual is corrected by Markov chain. The accuracy of model is researched under three typical weather conditions. The results show that this model gains the high precision, and the efficiency is comparatively better in sunny and cloudy condition than low irradiance. The proposed method reflects the actual trend of the PV generations, which can be successfully applied to engineering and scientific research.