Electrical Behavior Association Mining for Household ShortTerm Energy
  Consumption Forecasting
Heyang Yu Yuxi Sun Yintao Liu Guangchao Geng Quanyuan Jiang
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.