Interpretable Modeling for Short- and Medium-Term Electricity Demand Forecasting

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.

Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight

Electricity demand forecasting plays a fundamental role in the operation and planning procedures of power systems, and the publications related to electricity demand forecasting have attracted more and more attention in the past few years. To have a better understanding of the knowledge structure in the field of electricity demand forecasting, we applied scientometric methods to analyze the current state and the emerging trends based on the 831 publications from the Web of Science Core Collection during the past 20 years (1999–2018). Employing statistical description analysis, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this study gives a comprehensive overview of the most critical countries, institutions, journals, authors, and publications in this field, cooperative networks relationships, research hotspots, and emerging trends. The results can provide meaningful guidance and helpful insights for researchers to enhance the understanding of crucial research, emerging trends, and new developments in electricity demand forecasting.