DocumentCode :
1207608
Title :
Analyzing the impact of weather variables on monthly electricity demand
Author :
Hor, Ching-Lai ; Watson, Simon J. ; Majithia, Shanti
Author_Institution :
Centre for Renewable Energy Syst. Technol., Loughborough Univ., UK
Volume :
20
Issue :
4
fYear :
2005
Firstpage :
2078
Lastpage :
2085
Abstract :
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°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.
Keywords :
load forecasting; power consumption; power markets; power system planning; regression analysis; England; UK; Wales; electricity consumption; electricity industry deregulation; electricity industry privatization; enthalpy latent day; load pattern; mean absolute percentage error; monthly electricity demand forecasting; multiple regression model; network infrastructure; power system planning; weather condition; Economic indicators; Electricity supply industry; Electricity supply industry deregulation; Energy consumption; Energy management; Load management; Predictive models; Privatization; Temperature dependence; Weather forecasting; Climatic variables; forecasting; load pattern; monthly demand; multiple regression;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/TPWRS.2005.857397
Filename :
1525139
Link To Document :
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