Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather

TL;DR

This paper presents a new photovoltaic forecasting model that effectively quantifies uncertainty during hazy weather, improving accuracy by integrating modified entropy, clustering, and attention mechanisms.

Contribution

The paper introduces a novel model combining modified entropy, clustering, and attention mechanisms for better PV forecasting during hazy weather, with optimized hyperparameters.

Findings

Significant improvement in forecasting accuracy over existing models.

Effective quantification of uncertainty during hazy weather.

Model demonstrates robustness across two datasets.

Abstract

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.