An Optimization Method of Genetic Algorithm for LSSVM in Medium Term Electricity Price Forecasting

Authors

  • Intan Azmira Wan Abdul Razak Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia
  • Izham Zainal Abidin College of Engineering, National Energy University, Selangor, Malaysia.
  • Yap Keem Siah College of Engineering, National Energy University, Selangor, Malaysia.
  • Aidil Azwin Zainul Abidin College of Engineering, National Energy University, Selangor, Malaysia.
  • Titik Khawa Abdul Rahman Faculty of Engineering Girl Campus, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Nurliyana Baharin Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia
  • Mohd. Hafiz Jali Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia

Keywords:

Genetic Algorithms, Least Square Support Vector Machines, Medium Term Price Forecasting, Optimization,

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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Published

2018-07-04

How to Cite

Wan Abdul Razak, I. A., Zainal Abidin, I., Keem Siah, Y., Zainul Abidin, A. A., Abdul Rahman, T. K., Baharin, N., & Jali, M. H. (2018). An Optimization Method of Genetic Algorithm for LSSVM in Medium Term Electricity Price Forecasting. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-5), 99–103. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4358

Issue

Vol. 10 No. 2-5: Embracing The 4th Industrial Revolution: Innovation in Telecommunication, Electronic & Computer Engineering I

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Articles

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