Prediction of long-term photovoltaic power generation in the context of climate change

Abstract

Accurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %.