Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-BP and GA-BP Neural Networks

Abstract

With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely used backpropagation (BP) and improved BP neural network algorithm in short-term output prediction of PV power stations own the drawbacks of neglection of meteorological factors and weather conditions in inputs. Meanwhile, the existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. Therefore, based on the PV power plant in Lijiang, considering the related factors that influence PV output such as solar irradiance, environmental temperature, atmospheric pressure, wind velocity, wind direction, and historical generation data of the PV power station, three neural network algorithms (i.e., BP, GA-BP, and PSO-BP) are utilized respectively in this work to construct a short-term forecasting model of PV output. Simulation results show that GA-BP and PSO-BP network forecasting models both obtain high prediction accuracy, which indicates GA and PSO methods can effectively reduce the prediction errors in contrast to the original BP model. In particular, PSO owns better applicability than GA, which can further reduce the errors of the PV power prediction model.