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• W2161623840 abstract "Purpose – The 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/approach – In 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.Findings – With 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 t..." @default.
• W2161623840 created "2016-06-24" @default.
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• W2161623840 date "2011-06-21" @default.
• W2161623840 modified "2023-09-26" @default.
• W2161623840 title "A more accurate benchmark for daily electricity demand forecasts" @default.
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• W2161623840 doi "https://doi.org/10.1108/01409171111146698" @default.
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