Daily prediction of solar power generation based on weather forecast information in Korea

Abstract

Solar panel photovoltaic (PV) systems are widely used in Korea to generate solar energy, which is one of the most promising renewable energy sources. With regard to solar electricity providers and a grid operator, it is critical to accurately predict solar power generation for supply–demand planning in an electrical grid, which directly affects their profit. This prediction is, however, a challenging task because solar power generation is weather dependent and uncontrollable. In this study, a daily prediction model based on the weather forecast information for solar power generation is proposed. In the case of the proposed model, the cloud and temperature data available from the weather forecast information is used to predict the amount of solar radiation as well as a loss adjustment factor to reflect the possible loss of power generation due to the degradation or failure of the PV module. Using the proposed model, solar power generation for the following day can be predicted. The proposed model is embedded into a solar PV monitoring system that is commercially used in Korea, and it is shown to perform better than the existing prediction models.