Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929244106391552 |
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| author | Yu, Heyang Sun, Yuxi Liu, Yintao Geng, Guangchao Jiang, Quanyuan |
| author_facet | Yu, Heyang Sun, Yuxi Liu, Yintao Geng, Guangchao Jiang, Quanyuan |
| contents | Accurate household short-term energy consumption forecasting (STECF) is crucial for home energy management, but it is technically challenging, due to highly random behaviors of individual residential users. To improve the accuracy of STECF on a day-ahead scale, this paper proposes an novel STECF methodology that leverages association mining in electrical behaviors. First, a probabilistic association quantifying and discovering method is proposed to model the pairwise behaviors association and generate associated clusters. Then, a convolutional neural network-gated recurrent unit (CNN-GRU) based forecasting is provided to explore the temporal correlation and enhance accuracy. The testing results demonstrate that this methodology yields a significant enhancement in the STECF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09433 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting Yu, Heyang Sun, Yuxi Liu, Yintao Geng, Guangchao Jiang, Quanyuan Signal Processing Artificial Intelligence Machine Learning Systems and Control Accurate household short-term energy consumption forecasting (STECF) is crucial for home energy management, but it is technically challenging, due to highly random behaviors of individual residential users. To improve the accuracy of STECF on a day-ahead scale, this paper proposes an novel STECF methodology that leverages association mining in electrical behaviors. First, a probabilistic association quantifying and discovering method is proposed to model the pairwise behaviors association and generate associated clusters. Then, a convolutional neural network-gated recurrent unit (CNN-GRU) based forecasting is provided to explore the temporal correlation and enhance accuracy. The testing results demonstrate that this methodology yields a significant enhancement in the STECF. |
| title | Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting |
| topic | Signal Processing Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2402.09433 |