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.

该研究提出了一种利用关联挖掘和卷积神经网络门控循环单元的新型家庭短期能源消耗预测 (STECF) 方法，通过建模行为关联和探索时间相关性，实现了日前比例尺上 STECF 的准确性的显著提升。

家庭短期能源消耗预测的电气行为关联挖掘

Electrical Behavior Association Mining for Household ShortTerm Energy  Consumption Forecasting

In this paper, the process of forecasting household energy consumption is
studied within the framework of the nonparametric Gaussian Process (GP), using
multiple short time series data. As we begin to use smart meter data to paint a
clearer picture of residential electricity use, it becomes increasingly
apparent that we must also construct a detailed picture and understanding of
consumer's complex relationship with gas consumption. Both electricity and gas
consumption patterns are highly dependent on various factors, and the intricate
interplay of these factors is sophisticated. Moreover, since typical gas
consumption data is low granularity with very few time points, naive
application of conventional time-series forecasting techniques can lead to
severe over-fitting. Given these considerations, we construct a stacked GP
method where the predictive posteriors of each GP applied to each task are used
in the prior and likelihood of the next level GP. We apply our model to a
real-world dataset to forecast energy consumption in Australian households
across several states. We compare intuitively appealing results against other
commonly used machine learning techniques. Overall, the results indicate that
the proposed stacked GP model outperforms other forecasting techniques that we
tested, especially when we have a multiple short time-series instances.

本文针对多个短时间序列数据中的房屋能源消耗预测问题，研究了在非参数高斯过程（GP）框架下的处理方法。文章使用堆叠式 GP 方法处理澳大利亚多个地区的实际数据，得出在有多个短时间序列时，该方法优于其他常用的机器学习技术的预测结果。