An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting

Intan Azmira Wan Abdul Razak, Izham Zainal Abidin, Keem Siah Yap, Aidil Azwin Zainul Abidin, Titik Khawa Abdul Rahman, Nurliyana Baharin, Hafiz Bin Jali

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 exhibited low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimisation technique of Genetic Algorithm (GA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimised LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. The 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-GA shows higher forecast accuracy with lower complexity than the existing models.

Original languageEnglish
Pages (from-to)99-103
Number of pages5
JournalJournal of Telecommunication, Electronic and Computer Engineering
Volume10
Issue number2-5
Publication statusPublished - 01 Jan 2018

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Support vector machines
Electricity
Genetic algorithms
Planning
Testing

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

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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 exhibited low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimisation technique of Genetic Algorithm (GA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimised LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. The 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-GA shows higher forecast accuracy with lower complexity than the existing models.",
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An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting. / Abdul Razak, Intan Azmira Wan; Zainal Abidin, Izham; Yap, Keem Siah; Zainul Abidin, Aidil Azwin; Rahman, Titik Khawa Abdul; Baharin, Nurliyana; Jali, Hafiz Bin.

In: Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10, No. 2-5, 01.01.2018, p. 99-103.

Research output: Contribution to journalArticle

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