Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather
This addresses uncertainty in solar energy forecasting for grid operators during hazy conditions, but appears incremental as it builds on existing methods with modifications.
The paper tackles the problem of forecasting photovoltaic power generation during hazy weather by proposing a novel model that uses modified entropy to qualify uncertainty, along with clustering and attention mechanisms. Experiments on two hazy weather datasets show the model significantly improves forecasting accuracy compared to existing models.
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 difficulty in qualifying the uncertainty of PV power generation, especially during hazy weather, makes forecasting challenging. This paper proposes a novel model to address the issue. We introduce a modified entropy to qualify uncertainty during hazy weather while clustering and attention mechanisms are employed to reduce computational costs and enhance forecasting accuracy, respectively. Hyperparameters were adjusted using an optimization algorithm. Experiments on two datasets related to hazy weather demonstrate that our model significantly improves forecasting accuracy compared to existing models.