Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting

Intan Azmira , Abdul Razak and Izham , Zainal Abidin and Keem Siah, Yap and Titik Khawa, Abdul Rahman (2014) Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting. Journal of Electrical Engineering, 15 (1). pp. 1-8. ISSN 1582-4594

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Abstract

Forecasting price has now become essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increasing the utility’s profit and energy efficiency as well. The main challenge in forecasting electricity price is when dealing with non-stationary and high volatile price series. Some of the factors influencing this volatility are load behavior, weather, fuel price and transaction of import and export due to long term contract. This paper proposes the use of Least Square Support Vector Machine (LSSVM) with Genetic Algorithm (GA) optimization technique to predict daily electricity prices in Ontario. The selection of input data and LSSVM’s parameter held by GA are proven to improve accuracy as well as efficiency of prediction. A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. However, future study should emphasize on improving forecast accuracy during spike event since Ontario power market is reported as among the most volatile market worldwide.

Item Type:	Article
Uncontrolled Keywords:	electricity price forecasting Least Square Support Vector Machine (LSSVM), Genetic Algorithm (GA), Particle Swarm Optimization (PSO)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electrical Engineering > Department of Diploma Studies
Depositing User:	INTAN AZMIRA WAN ABDUL RAZAK
Date Deposited:	28 Sep 2015 01:07
Last Modified:	28 Sep 2015 01:07
URI:	http://eprints.utem.edu.my/id/eprint/14821
Statistic Details:	View Download Statistic

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