Econometric Analysis on Beijing Temperature Influence upon Electricity Load

The variation of electricity load is influenced by the weather, especially the temperature. Relationship model between them is established by using of econometrics method, and its predictive power is assessed by forecasting a monthly and a quarterly load.The mean daily temperature from Beijing Weather Observatory and daily maximum electricity load from Beijing Electricity Company during 2002—2004 are collected to form the model. Because of the temperature's nonlinear affect on load, the heating degree days (HDD) and the cooling degree days (CDD), which are the derivation index of temperature, are used as explanatory variables to establish a concise linear model. Degree days index is defined analogically as the accumulated Celsius degrees between a threshold temperature and the daily mean temperature. The HDD is a good estimation of an accumulated cold during the cold season and the CDD estimates an accumulated warmth during the warm season. The 18 ℃ threshold temperature is chosen in Beijing.The development trend, the different influence on load of different month, different day (holiday and workday), as well as the lag-effect on load of HDD and CDD are fully considered in the model. The errors autoregressive structure is introduced. The test results and actual data have a good fitting degree, R² is up to 95%, and DW is 2.The CDD has a stronger influence on electricity load than the HDD. If the CDD increases 1 ℃, the electricity load will increase 3%; the HDD increases 1 ℃, the load will only increase 0. 4%. CDD's lag-effect is also stronger than HDD's. The electricity load on holidays, such as Saturdays, Sundays, the May Day holiday and the National Day, is 3%—4% lower than workdays, in the Spring Festival, it is even lower. Assessment of its predictive power shows that it works good for the medium prediction of electricity load, systematic errors of seasonal forecasting is 5. 4% at 99. 7% confidence level.