Interpretable modeling for short- and medium-term electricity load forecasting
Interpretable modeling for short- and medium-term electricity load forecasting
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发表时间：
2020-06
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通讯作者：
K. Hirose
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作者：
K. Hirose
我们通过使用过去的负荷和每日天气预报信息来考虑短期和中期电力负荷预测的问题。传统上，许多研究人员直接应用回归分析。然而，利用现有方法很难解释天气对这些载荷的影响。在这项研究中，我们建立了一个统计模型来解决这个解释问题。具有基础扩展的变系数模型用于捕捉天气效应的非线性结构。当回归系数非负时，此方法会产生可解释的模型。为了估计非负回归系数，我们采用非负最小二乘法。三个真实数据分析显示了我们提出的统计模型的实用性。其中两个证明了我们提出的方法具有良好的预测准确性和可解释性。在第三个示例中，我们研究了 COVID-19 对电力负荷的影响。这一解释将有助于制定节能干预和需求响应策略。
We consider the problem of short- and medium-term electricity load forecasting by using past loads and daily weather forecast information. Conventionally, many researchers have directly applied regression analysis. However, interpreting the effect of weather on these loads 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 loads. The interpretation would help make strategies for energy-saving interventions and demand response.