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

TL;DR

This paper introduces an interpretable statistical model for short- and medium-term electricity load forecasting that effectively captures nonlinear weather effects and aids in strategic energy management.

Contribution

It develops a varying coefficient model with basis expansion and nonnegative least squares to improve interpretability and accuracy in load forecasting.

Findings

The model achieves good forecast accuracy in real data analyses.

It provides clear interpretation of weather effects on electricity loads.

The approach helps analyze impacts like COVID-19 on energy consumption.

Abstract

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.