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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.17700 |
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| _version_ | 1866912552059928576 |
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| author | Yang, Junying Lu, Gang Yan, Xiaoqing Xia, Peng Wu, Di |
| author_facet | Yang, Junying Lu, Gang Yan, Xiaoqing Xia, Peng Wu, Di |
| contents | Machine learning (ML) is capable of accurate Load Forecasting from complete data. However, there are many uncertainties that affect data collection, leading to sparsity. This article proposed a model called Adaptive Ensemble Learning with Gaussian Copula to deal with sparsity, which contains three modules: data complementation, ML construction, and adaptive ensemble. First, it applies Gaussian Copula to eliminate sparsity. Then, we utilise five ML models to make predictions individually. Finally, it employs adaptive ensemble to get final weighted-sum result. Experiments have demonstrated that our model are robust. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17700 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting Yang, Junying Lu, Gang Yan, Xiaoqing Xia, Peng Wu, Di Machine Learning Machine learning (ML) is capable of accurate Load Forecasting from complete data. However, there are many uncertainties that affect data collection, leading to sparsity. This article proposed a model called Adaptive Ensemble Learning with Gaussian Copula to deal with sparsity, which contains three modules: data complementation, ML construction, and adaptive ensemble. First, it applies Gaussian Copula to eliminate sparsity. Then, we utilise five ML models to make predictions individually. Finally, it employs adaptive ensemble to get final weighted-sum result. Experiments have demonstrated that our model are robust. |
| title | Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.17700 |