Electricity market price forecasting using support vector machines

Sun, Yi, Bansal, R. C., Bhardwaj, A. K. and Srivastava, A. K. (2011) Electricity market price forecasting using support vector machines. International Journal of Computer Aided Engineering and Technology, 3 1: 1-18. doi:10.1504/IJCAET.2011.037865

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Author Sun, Yi
Bansal, R. C.
Bhardwaj, A. K.
Srivastava, A. K.
Title Electricity market price forecasting using support vector machines
Journal name International Journal of Computer Aided Engineering and Technology   Check publisher's open access policy
ISSN 1757-2657
Publication date 2011
Sub-type Article (original research)
DOI 10.1504/IJCAET.2011.037865
Volume 3
Issue 1
Start page 1
End page 18
Total pages 18
Editor Yan Luo
Place of publication Olney, Bucks, United Kingdom
Publisher Inderscience Publishers
Collection year 2012
Language eng
Formatted abstract
Due to the electricity market deregulation, the techniques used for load forecasting have gradually improved over the years. Deregulation in the power system industry has caused rising requirement in planning, operating and controlling electric energy systems, which brings electricity load forecasting to a crucial level. Therefore, adequate techniques are desired for accurately predicting the load and hence assisting power companies in generating capacity scheduling, maintenance, energy planning and procurement, etc. An accurate forecast can greatly help power distribution companies to improve their electricity marketing strategies and avoid over or under unitisation of generating capacity and therefore optimises energy prices. But, to predict the load demand in real time requires a considerable amount of efforts. This paper presents a design methodology for a short term load forecasting useful for distribution companies, which are capable of interacting with users, gathering historical load data, performing a statistical analysis on the historical data and plotting graphs of the predicted load using the support vector machine (SVM). SVM is the chosen forecasting technique because many studies have concluded that SVMs produce the optimum accuracy as compared to other methods such as Naive Bayesian, but SVM has not been optimised for the domain.
Keyword Support vector machines
Short term load forecasting
Electricity markets
Price forecasting
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Created: Fri, 25 Mar 2011, 18:37:58 EST by Dr Ramesh Bansal on behalf of School of Information Technol and Elec Engineering