A Seasonal Model Using Optimized Multi-Layer Neural Networks to Forecast Power Output of PV Plants

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With the continuous increase of grid-connected photovoltaic (PV) installed capacity and the urgent demand of synergetic utilization with the other power generation forms, the high-precision prediction of PV power generation is increasingly important for the optimal scheduling and safe operation of the grid. In order to improve the power prediction accuracy, using the response characteristics of PV array under different environmental conditions, a data driven multi-model power prediction method for PV power generation is proposed, based on the seasonal meteorological features. Firstly, through the analysis of PV power characteristics in typical seasons and seasonal distribution of the weather factors, such as solar irradiance and ambient temperature, the influences of different weather factors on PV power prediction are studied. Then, according to the meteorology characteristics of Beijing, different seasons can be divided. The historical data corresponding to different seasons are acquired and then the seasonal PV power forecasting models are established based on optimized multi-layer back propagation neural network (BPNN), realizing the multi-model prediction of PV power. Finally, effectiveness of the seasonal PV power forecasting method is compared and validated. The performance analysis of the neural network forecasting model under typical seasonal conditions shows that the multi-model forecasting method based on seasonal characteristics of PV power generation is better than that of single power forecasting model for the whole year. The results show that the proposed method can effectively improve the power forecasting accuracy of PV power.