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Study On Characteristics Of Electric Load Of Pudong And Itsrelationship With Meteorological Elements

Posted on:2006-03-31Degree:MasterType:Thesis
Country:ChinaCandidate:Y Q ZhangFull Text:PDF
GTID:2120360272962264Subject:Science of meteorology
Abstract/Summary:PDF Full Text Request
Using the electric load data and meteorological element observations for Pudong during the period of 1 January 2001-30 June 2003, the relationship between hourly electric load and meteorological elements including temperature, pressure, relative humidity and sunshine duration was examined. Besides, the response of the hourly electric load to the variations of several meteorological elements was quantitatively analyzed. After understanding the variety regulation of electric load and meteorological elements and correlate relations between them, Adopt the regression analysis to select the forecast factors, building the regression forecast equation of the weather elements and electric load. In the process of build these forecast equations, not only according to the meteorological elements from the true record conditions forecast that the electric load in the certain time of future, but also using the meteorological elements from the result of model MM5 to forecast the same period electric load. As a result, make ensemble of the two kinds of different forecast method. The conclusion is: The electric load has the obvious seasonal, and very difference in the workday and holiday. It had high relativity between the electric load and temperature, degree of humidity etc. in summer, the second was in winter, but at that in spring and autumn then is not obvious. Using meteorological loads in the forecast equation was better than used the original load, because it rise greatly in accuracy of forecasting. The result of two kinds of forecast method ensemble was better than use the result of only one method; the ensemble forecast equation could satisfy the business demand.
Keywords/Search Tags:Shanghai Pudong, Electricity quantity, Meteorological elements, Correlation analysis, Regression analysis, Ensemble predictions
PDF Full Text Request
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