Analyzing the impact of weather variables on monthly electricity demand

• Published in: IEEE transactions on power systems Vol. 20; no. 4; pp. 2078 - 2085
• Main Authors: Ching-Lai Hor, Watson, S.J., Majithia, S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Climatic variables; Economic indicators; Electricity supply industry; Electricity supply industry deregulation; Energy consumption; Energy management; forecasting; Load management; load pattern; monthly demand; multiple regression; Predictive models; Privatization; Temperature dependence; Weather forecasting
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2005.857397

The electricity industry is significantly affected by weather conditions both in terms of the operation of the network infrastructure and electricity consumption. Following privatization and deregulation, the electricity industry in the U.K. has become fragmented and central planning has largely disappeared. In order to maximize profits, the margin of supply has decreased and the network is being run closer to capacity in certain areas. Careful planning is required to manage future electricity demand within the framework of this leaner electricity network. There is evidence that the climate in the U.K. is changing with a possible 3/spl deg/C average annual temperature increase by 2080. This paper investigates the impact of weather variables on monthly electricity demand in England and Wales. A multiple regression model is developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. The average mean absolute percentage error (MAPE) for the worst model is approximately 2.60% in fitting the monthly electricity demand from 1989 to 1995 and approximately 2.69% in the forecasting over the period 1996 to 2003. This error may reflect the nonlinear dependence of demand on temperature at the hot and cold temperature extremes; however, the inclusion of degree days, enthalpy latent days, and relative humidity in the model improves the demand forecast during the summer months.