Data-Driven Modeling and Analysis for Solar Generation Considering the Post-Snow Day Meteorology Factors

As the global trend towards decarbonization and decentralization of power systems accelerates, the adoption of solar photovoltaic (PV) systems has seen a significant increase at both utility and residential levels. However, the reliability of power grids in colder regions, particularly those with heavy snowfall, could be at risk due to substantial reductions in solar power generation caused by snow cover. The performance of PV systems can be adversely affected by snow for prolonged periods, and the rate of recovery is primarily dependent on post-snow climatic conditions. This research aims to quantify the impact of snow on PV generation considering meteorological factors in the days following a snow event. A data-driven analysis is presented, with empirical data employed to evaluate the influence of snowfall on PV system performance. Additionally, a multilinear regression model is proposed to predict PV solar power generation under snow-affected conditions. This study not only provides a better understanding of the interaction between snowfall and PV systems but also offers a novel forecasting tool that aids in enhancing the resilience and reliability of power grids in cold regions.