Optimizing meteorological predictions to improve photovoltaic power generation in coastal areas

Abstract

Photovoltaic (PV) power generation is widely considered as the most important way to reduce energy carbon emissions. Accurate prediction of PV power remains a significant challenge in coastal areas with high population density, primarily due to the limitations in regional weather forecasting. In this study, we present an optimal selection strategy of typical meteorological parameters for PV power prediction in the Yangtze River Delta region of China, one of the highest electricity demand regions in the world. We find that evaporation and relative humidity are the most noteworthy meteorological factors in PV power prediction influencing coastal areas, with correlation coefficients of −0.77 and −0.52, respectively. PV power prediction is improved by ∼30 % by incorporating weather forecasting with appropriate meteorological parameters, especially under thicker cloud conditions. This improvement of PV prediction by meteorological selection not only aids in optimizing energy distribution but also plays a crucial role in reducing carbon emissions.