The Analysis And Forecasting Of Electricity Demand In Guang Dong Province

During the10th Five-Year Plan period, Guangdong’s economy has been rapiddevelopment, Accompanied by the growth of electricity consumption of industry and varioustypes of other demand. Meanwhile, electrical power supply and demand is in a seriousimbanlance. Especially in recent years, Guangdong electric power provision develops quickly,but the demand for electricity increase faster, the contradiction between supply and demandbecome conspicuous, and the power gap between supply and demand frequentlyappears.Therefore,in odre to achive the sustainable development of the electric powerindustry and ensure that the development of electric power is adapt to the requirementsofnational economic and social development, correct and reasonable electricity demandingforecast should be implemented, the forecasting not only can guide resource allocation andplanning, but also provide the value of the reference for future economic policy.Firstly, based on the electricity demand influencing factors analysis, we discuss therelationship between the influencing factors to the electricity demand of Guangdong Provincesuch as GDP, industrial structure, technological progress, the temperature and so on. Aftertaking the unit root test for these variables, the cointegration model is put forward to analyzethe cointegration relationship between the explained variable and explanatory variables.On the basis of analysis of electricity demand influencing factors,we compose the leadingindex and coincident index,using the way of composing economic sentiment from U.S.Department of Commerce,then predict the trend of power demand fluctuations according tothe leading index.In terms of numerical prediction of electricity consumption, three methods are used: oneis the mathematical statistic methods, an other one is artificial intelligence methods, and thelast method is the combination of mathematical statistic model and artificial intelligencemodel. Over all, it is found that the combination model achives greater predictting and fittingaccuracy.To conclude, this paper provide two way to forecasting electrical demand from twodifferent aspects.On one hand,we get the changes of the trend of electricity consumptiongrowth rate,and on the other hand,we get accurate value of electricity forecast,the twoacquisition achieved a high accuracy and precision,whici is significant to the guidance ofpower forecasting work.