A more accurate benchmark for daily electricity demand forecasts

Abstract

PurposeThe purpose of this paper is to propose a simple regression‐based method of forecasting daily electricity demand, which may serve as a more accurate benchmark for short‐term forecasts.Design/methodology/approachIn order to make more efficient use of the calendar effects in electricity demand, including weekend, and seasonal effects, while maintaining the parsimony of the forecasting model, the authors match the demand on each day of an entire year with the average of the corresponding days in recent years. This matching‐day approach substantially simplifies the modeling procedure of complex periodicity in electricity demand without loss of information.FindingsWith daily data on electric power system load in New England, the authors' method provides quite accurate forecasts. The mean absolute percentage error (MAPE) (2.1 percent) is significantly lower than those of the seasonal ARIMA and exponential smoothing method, and also comparable to the performance of more sophisticated methods in the literature.Research limitations/implicationsThe authors' method needs to be modified or augmented by other techniques when the periodicity is not stable due to time trends, economic crises, and other factors.Practical implicationsThe management of electric utility providers as well as professional forecasters may use this method as a handy benchmark.Originality/valueWhile previous studies focus mainly on accuracy of forecasts, the method presented in the paper is developed with the balance between accuracy and ease of use in mind.

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