Interpretable Modeling for Short- and Medium-Term Electricity Demand Forecasting
Interpretable Modeling for Short- and Medium-Term Electricity Demand Forecasting
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DOI：
10.3389/fenrg.2021.724780
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发表时间：
2021-12-14
影响因子：
3.4
通讯作者：
Hirose, Kei
中科院分区：
文献类型：
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作者：
Hirose, Kei
我们考虑的问题，短期和中期的电力需求预测，通过使用过去的需求和每日的天气预报信息。传统上，许多研究人员直接应用回归分析。然而，解释天气对需求的影响是困难的与现有的方法。在这项研究中，我们建立了一个统计模型，解决了这个解释问题。一个基展开的变系数模式被用来捕捉天气效应的非线性结构。当回归系数为非负时，这种方法得到了一个可解释的模型。为了估计非负回归系数，我们采用非负最小二乘。三个真实的数据分析表明，我们提出的统计建模的实用性。其中两个证明了我们所提出的方法具有良好的预测精度和可解释性。在第三个例子中，我们调查了COVID-19对电力需求的影响。这种解释将有助于制定节能干预和需求响应战略。
We consider the problem of short- and medium-term electricity demand forecasting by using past demand and daily weather forecast information. Conventionally, many researchers have directly applied regression analysis. However, interpreting the effect of weather on the demand is difficult with the existing methods. In this study, we build a statistical model that resolves this interpretation issue. A varying coefficient model with basis expansion is used to capture the nonlinear structure of the weather effect. This approach results in an interpretable model when the regression coefficients are nonnegative. To estimate the nonnegative regression coefficients, we employ nonnegative least squares. Three real data analyses show the practicality of our proposed statistical modeling. Two of them demonstrate good forecast accuracy and interpretability of our proposed method. In the third example, we investigate the effect of COVID-19 on electricity demand. The interpretation would help make strategies for energy-saving interventions and demand response.