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

2024/07/29 by Yang Xuan, Yang, Xuan, Yunxuan Dong +3 · 1 citation
Energy · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Power Systems and Renewable Energy #Signal Processing (eess.SP) #Smart Grid and Power Systems #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2407.19663

openalex publication_date 2024/07/29 · openalex created_date 2024/08/01 · openalex updated_date 2026/07/28

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.

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