A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting

Intan Azmira Wan Abdul Razak, Nik Nur Atira Nik Ibrahim, Izham Zainal Abidin, Yap Keem Siah, Aidil Azwin Zainul Abidin, Titik Khawa Abdul Rahman

Research output: Contribution to journalArticle

Abstract

Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibit low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimization technique of Bacterial Foraging Optimization Algorithm (BFOA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. Monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index are selected as the input features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than the existing models.

Original languageEnglish
Pages (from-to)232-239
Number of pages8
JournalInternational Journal of Integrated Engineering
Volume11
Issue number3
DOIs
Publication statusPublished - 01 Jan 2019

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All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Materials Science (miscellaneous)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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