  Solar energy is one of the most promising renewable energy resources.
Forecasting photovoltaic power generation is an important way to increase
photovoltaic penetration. However, the task of photovoltaic forecasting is
complicated due to its property of uncertainty, especially in specific regions
during the foggy winter. This paper proposes a novel model to accomplish the
problem. A developed entropy is created to qualify the uncertainty during the
foggy winter. The clustering method and modified retention network are applied
to reduce complexity and forecast, respectively. We adopt an optimization to
optimize the hyperparameters. Results are validated from the multivariate
forecasting model using the dataset from a photovoltaic power station in
Jiangsu Province, China. Experiments show that the proposed model improves the
forecasting accuracy compared to various models during the foggy winter.
