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We consider the problem of short- and medium-term electricity loadforecasting 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 withthe existing methods. In this study, we build a statistical model that resolvesthis interpretation issue. A varying coefficient model with basis expansion isused to capture the nonlinear structure of the weather effect. This approachresults in an interpretable model when the regression coefficients arenonnegative. To estimate the nonnegative regression coefficients, we employnonnegative least squares. Three real data analyses show the practicality ofour proposed statistical modeling. Two of them demonstrate good forecastaccuracy and interpretability of our proposed method. In the third example, weinvestigate the effect of COVID-19 on electricity loads. The interpretationwould help make strategies for energy-saving interventions and demand response.

Interpretable modeling for short- and medium-term electricity load forecasting