Buildings are typically equipped with smart meters to measure electricity demand at regular intervals. Smart meter data for a single building have many uses, such as forecasting and assessing overall building performance. However, when data are available from multiple buildings, there are additional applications that are rarely explored. For instance, we can explore how different building characteristics influence energy demand. If each building is treated as a random effect and building characteristics are handled as fixed effects, a mixed-effects model can be used to estimate how characteristics affect energy usage. In this paper, we demonstrate that producing 1-day-ahead demand predictions for 123 commercial office buildings using mixed models can improve forecasting accuracy. We experiment with random intercept, random intercept and slope and non-linear mixed models. The predictive performance of the mixed-effects models are tested against naive, linear and non-linear benchmark models fitted to each building separately. This research justifies using mixed models to improve forecasting accuracy and to quantify changes in energy consumption under different building configuration scenarios.

中文翻译：

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用于智能电表需求时间序列预测的非线性混合效应模型

建筑物通常配备智能电表，以定期测量电力需求。单个建筑物的智能电表数据有多种用途，例如预测和评估整体建筑物性能。但是，当可以从多个建筑物获得数据时，就会出现很少探索的其他应用程序。例如，我们可以探索不同的建筑特征如何影响能源需求。如果每个建筑物都被视为随机效应，而建筑物特性则被视为固定效应，则可以使用混合效应模型来估计特性如何影响能源使用。在本文中，我们证明使用混合模型对 123 座商业办公楼进行 1 天前的需求预测可以提高预测准确性。我们试验随机截距，随机截距和斜率以及非线性混合模型。混合效应模型的预测性能分别针对适合每个建筑物的朴素、线性和非线性基准模型进行测试。这项研究证明使用混合模型来提高预测准确性并量化不同建筑配置场景下的能源消耗变化是合理的。