论文标题
短期和中期电力负载预测的可解释建模
Interpretable modeling for short- and medium-term electricity load forecasting
论文作者
论文摘要
我们考虑通过使用过去的负载和日常天气预报信息来预测短期和中期电力负载的问题。从传统上讲，许多研究人员已直接应用回归分析。但是，很难用现有方法来解释天气对这些负载的影响。在这项研究中，我们建立了一个解决此解释问题的统计模型。具有基础扩展的不同系数模型用于捕获天气效应的非线性结构。当回归系数不负时，这种方法会导致可解释的模型。为了估计非负回归系数，我们采用了非负平方。三个实际数据分析显示了我们提出的统计建模的实用性。其中两个证明了我们提出的方法的良好预测准确性和解释性。在第三个示例中，我们研究了Covid-19对电力负荷的影响。该解释将有助于制定节能干预措施和需求响应的策略。
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.
