Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting

• URL: http://arxiv.org/abs/2402.09433v1
• Date: Fri, 26 Jan 2024 03:23:09 GMT
• Title: Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting
• Authors: Heyang Yu, Yuxi Sun, Yintao Liu, Guangchao Geng, Quanyuan Jiang
• Abstract summary: This paper proposes a novel STECF methodology that leverages association mining in electrical behaviors. A convolutional neural network-gated recurrent unit (CNN-GRU) based forecasting is provided to explore the temporal correlation and enhance accuracy.
• Score: 0.7474723197425266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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