DocumentCode
3313166
Title
Long-Term Load Forecast Using Decision Tree Method
Author
Ding, Qia
Author_Institution
Dept. of PSCC, Nanjing Autom. Res. Inst.
fYear
2006
fDate
Oct. 29 2006-Nov. 1 2006
Firstpage
1541
Lastpage
1543
Abstract
Accurate long-term load forecast is important in planning, especially for developing countries where the demand is increased with dynamic and high growth rate. As a data mining method, the decision tree is very useful in a sense that it allows to extract if-then rules and clarify the relationship between input and output variables easily. In this paper, a decision tree based method was used to clarify the relationship between the load and relative variables. A set of decision rules were obtained and stored in the knowledge base to forecast. Finally comparative forecasting results with a conventional approach in real system are presented, demonstrating the usefulness of the developed system
Keywords
data mining; decision trees; load forecasting; power engineering computing; power system planning; data mining method; decision rules set; decision tree method; load variables; long-term load forecasting; power system planning; relative variables; Buildings; Classification tree analysis; Data mining; Decision trees; Economic forecasting; Information entropy; Load forecasting; Machinery; Power generation economics; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Systems Conference and Exposition, 2006. PSCE '06. 2006 IEEE PES
Conference_Location
Atlanta, GA
Print_ISBN
1-4244-0177-1
Electronic_ISBN
1-4244-0178-X
Type
conf
DOI
10.1109/PSCE.2006.296529
Filename
4075968
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