An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.

Original languageEnglish
Pages (from-to)748-755
Number of pages8
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume10
Issue number2
DOIs
Publication statusPublished - 01 May 2018

Fingerprint

Least Squares Support Vector Machine
Foraging
Electricity
Support vector machines
Forecasting
Optimization Algorithm
Forecast
Real-time Systems
Bidding
Optimization
Parameter Optimization
Power System
Low Complexity
Real time systems
Planning
Market
Model

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

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title = "An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm",
abstract = "Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.",
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An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm. / Razak, Intan Azmira Wan Abdul; Zainal Abidin, Izham; Yap, Keem Siah; Zainul Abidin, Aidil Azwin; Rahman, Titik Khawa Abdul; Baharin, Nurliyana; Jali, Mohd Hafiz Bin.

In: Indonesian Journal of Electrical Engineering and Computer Science, Vol. 10, No. 2, 01.05.2018, p. 748-755.

Research output: Contribution to journalArticle

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AU - Razak, Intan Azmira Wan Abdul

AU - Zainal Abidin, Izham

AU - Yap, Keem Siah

AU - Zainul Abidin, Aidil Azwin

AU - Rahman, Titik Khawa Abdul

AU - Baharin, Nurliyana

AU - Jali, Mohd Hafiz Bin

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N2 - Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.

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