Short-Term Solar Power Forecasting via General Regression Neural Network with Grey Wolf Optimization

Abstract

With the increasing awareness of environmental protection and the support of national policy, as well as the maturing of solar power generation technology, solar power generation has become one of the most promising renewable energies. However, due to changes in external factors such as season, time, weather, cloud cover, etc., solar radiation is uncertain, and it is difficult to predict energy output, even for the next hour. This inherent instability is a particularly difficult issue for the prediction of energy output in the effective operation of solar power systems. This paper proposes a grey wolf optimization (GWO)-based general regression neural network (GRNN), which is expected to provide more accurate predictions with shorter computational times. Therefore, a self-organizing map (SOM) is utilized to realize the weather clustering and the training of the GRNN with a GWO model. The performance of the proposed model is investigated using short-term and ultra-short-term forecasting in different seasons. It is very important to accurately predict the PV power output. Moreover, the numerical results demonstrate that the proposed approach can significantly enhance the prediction accuracy of PV systems.