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Modelling weather effects for impact analysis of residential time-of-use electricity pricing

机译：建模天气影响，以分析住宅使用时电价

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摘要

Analyzing the impact of pricing policies such as time-of-use (TOU) is challenging in the presence of confounding factors such as weather. Motivated by a lack of consensus and model selection details in prior work, we present a methodology for modelling the effect of weather on residential electricity demand. The best model is selected according to explanatory power, out-of-sample prediction accuracy, goodness of fit and interpretability. We then evaluate the effect of mandatory TOU pricing in a local distribution company in southwestern Ontario, Canada. We use a smart meter dataset of over 20,000 households which is particularly suited to our analysis: it contains data from the summer before and after the implementation of TOU pricing in November 2011, and all customers transitioned from tiered rates to TOU rates at the same time. We find that during the summer rate season, TOU pricing results in electricity conservation across all price periods. The average demand change during on-peak and mid-peak periods is 2.6% and 2.4% respectively. Changes during off-peak periods are not statistically significant. These TOU pricing effects are less pronounced compared to previous studies, underscoring the need for clear, reproducible impact analyses which include full details about the model selection process.
机译：在存在天气等混杂因素的情况下，分析诸如使用时间（TOU）之类的定价政策的影响具有挑战性。由于先前工作中缺乏共识和模型选择的细节，我们提出了一种模拟天气对居民用电需求影响的方法。根据解释力，样本外预测准确性，拟合优度和可解释性选择最佳模型。然后，我们在加拿大安大略省西南部的一家本地分销公司中评估强制性TOU定价的效果。我们使用了超过20,000个家庭的智能电表数据集，该数据集特别适合我们的分析：该数据集包含2011年11月实施TOU定价前后的夏季数据，并且所有客户同时从分层费率转换为TOU费率。我们发现，在夏季费率季节，用电设备的定价会导致所有价格时段的节电。高峰时段和高峰时段的平均需求变化分别为2.6％和2.4％。非高峰时段的变化在统计上并不显着。与以前的研究相比，这些TOU定价影响不那么明显，强调需要清晰，可重复的影响分析，其中包括有关模型选择过程的完整详细信息。

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