A novel method of BFOA-LSSVM for electricity price forecasting

Intan Azmira Wan Abdul Razak, Izham Zainal Abidin, Keem Siah Yap, Aidil Azwin Zainul Abidin, Titik Khawa Abdul Rahman, Arfah Ahmad

Research output: Contribution to journalArticle

Abstract

Forecasting price has now become an 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 increase the utilitys profit and energy efficiency. This paper proposes a novel method of Least Square Support Vector Machine (LSSVM) with Bacterial Foraging Optimization Algorithm (BFOA) to predict daily electricity prices in Ontario. The selection of input data and LSSVM's parameters held by BFOA are proven to improve accuracy as well as efficiency of prediction. A comparative study of the proposed method with previous researches was conducted in term of forecast accuracy. The results indicate that (1) the LSSVM with BFOA outperforms other methods for same test data; (2) the optimization algorithm of BFOA gives better accuracy than other optimization techniques. In fact, the proposed approach is less complex compared to other methods presented in this paper.

Original languageEnglish
Pages (from-to)4961-4968
Number of pages8
JournalARPN Journal of Engineering and Applied Sciences
Volume11
Issue number8
Publication statusPublished - 20 Apr 2016

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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