Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912063747522560 |
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| author | Yang, Xuan Dong, Yunxuan Yang, Lina Wu, Thomas |
| author_facet | Yang, Xuan Dong, Yunxuan Yang, Lina Wu, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19663 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather Yang, Xuan Dong, Yunxuan Yang, Lina Wu, Thomas Machine Learning Signal Processing 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. |
| title | Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2407.19663 |