Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting

arXiv:2402.09433v1 Announce Type: cross
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 …

abstract accuracy arxiv association behavior consumption cs.ai cs.lg cs.sy eess.sp eess.sy energy energy management forecasting home management methodology mining novel paper random scale type