DocumentCode :
3471361
Title :
Support vector machines with similar day’s training sample application in short-term load forecasting
Author :
Chang-chun, CAI ; Min, Wu
Author_Institution :
Dept. of Comput. & Inf. Eng., Hohai Univ., Changzhou
fYear :
2008
fDate :
6-9 April 2008
Firstpage :
1221
Lastpage :
1225
Abstract :
A novel method is proposed based on support vector machines using similar day´s load data as the training sample data for power load forecasting in this paper, where the power load has the specificity of periodicity and randomness. Analyzing the natural characteristic of power load and the forecasting day´s weather features and getting the calculating data for load forecasting. Quantifying the forecasting day´s weather features and calculating the similar degree between the similar days and forecasting day, choosing the most properly data as the training data for SVM according to the similar degree between them. The support vector machines is a novel learning method based on dimension and local minima and the new load forecasting model is based on SVM, whose parameters and kernel function is properly chosen for different conditions. And similar day´s training sample data for SVM can improve the degree of belief of original data and reduce the training time which is most important for load forecasting. The simulation results show that the novel method based on SVM using similar day´s training sample has faster speed, higher precision than other method and which proves that it is an effective method.
Keywords :
learning (artificial intelligence); load forecasting; power engineering computing; support vector machines; SVM; learning method; short-term load forecasting; similar days load data; support vector machines; training sample application; weather feature forecasting; Kernel; Learning systems; Linear regression; Load forecasting; Load modeling; Power system management; Predictive models; Support vector machines; Training data; Weather forecasting; Load forecasting; SVM; similar days kernels;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on
Conference_Location :
Nanjuing
Print_ISBN :
978-7-900714-13-8
Electronic_ISBN :
978-7-900714-13-8
Type :
conf
DOI :
10.1109/DRPT.2008.4523593
Filename :
4523593
Link To Document :
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