Study on Probabilistic Load Forecasting Model and Its Improvements
The current research on probabilistic load forecasting models mostly combines machine learning algorithms and quantile regression methods to construct quantile models. This paper first summarizes and combs the commonly used quantile regression forecast models. Combined with the actual data, we analy...
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| Veröffentlicht in: | Journal of physics. Conference series 2023-06, Vol.2527 (1), p.12073 |
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| container_title | Journal of physics. Conference series |
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| creator | Chen, Huican Chao, Zhu Duan, Qinwei Tang, Xuchen Xie, Xiangzhong Lai, Xiaowen Wen, Yakun |
| description | The current research on probabilistic load forecasting models mostly combines machine learning algorithms and quantile regression methods to construct quantile models. This paper first summarizes and combs the commonly used quantile regression forecast models. Combined with the actual data, we analyzed the main factors affecting the performance of probabilistic load forecasting and attempted to elaborate on the influencing mechanism. Then, a quantile regression probabilistic load forecasting strategy based on sample weight is designed. According to the analysis and experimental verification, we propose the future research direction for probabilistic load forecasting. |
| doi_str_mv | 10.1088/1742-6596/2527/1/012073 |
| format | Article |
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| source | Institute of Physics Open Access Journal Titles; EZB-FREE-00999 freely available EZB journals; IOPscience extra; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry |
| subjects | Algorithms Forecasting Machine learning Mathematical models Physics Regression Statistical analysis |
| title | Study on Probabilistic Load Forecasting Model and Its Improvements |
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