Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model

Electric machine theory; Forecasting; Optimization; Support vector machines; Bacterial Foraging Optimization Algorithm (BFOA); Bacterial foraging optimization algorithms; Day-ahead price forecasts; Forecasting accuracy; Least square support vector machines; Multi-stage optimization; Optimization lev...

Full description

Saved in:
Bibliographic Details
Main Authors: Intan Azmira W.A.R., Ahmad A., Abidin I.Z., Yap K.S., Nasir M.N.M., Upkli W.R.
Other Authors: 56602467500
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Tenaga Nasional
id my.uniten.dspace-25230
record_format dspace
spelling my.uniten.dspace-252302023-05-29T16:07:28Z Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model Intan Azmira W.A.R. Ahmad A. Abidin I.Z. Yap K.S. Nasir M.N.M. Upkli W.R. 56602467500 55336187300 35606640500 24448864400 55658799800 57217213893 Electric machine theory; Forecasting; Optimization; Support vector machines; Bacterial Foraging Optimization Algorithm (BFOA); Bacterial foraging optimization algorithms; Day-ahead price forecasts; Forecasting accuracy; Least square support vector machines; Multi-stage optimization; Optimization levels; Uncertain condition; Power markets Predicting the price of electricity is an important aspect in the operation and planning of power systems. However, predicting the price of electricity is a relatively challenging task as it faces very uncertain conditions. Hence, this study proposes a hybrid Least Square Support Vector Machine (LSSVM) and Bacterial Foraging optimization Algorithm (BFOA) for day-ahead electricity price forecast. The main contribution of this work is the multistage optimization approach of LSSVM-BFOA that can improve the forecasting accuracy and efficiency. This is achieved by optimizing the input features and parameters of LSSVM at the same time. The input features have been reduced by six optimization levels in order to avoid losing any significant input. At the same time, the average MAPE is observed and the second stage of optimization is carried out. These processes are performed until there is no improvement in MAPE is observed. This model is examined in the Ontario power market. The LSSVM-BFOA model developed showed higher prediction accuracy with less complex model structure than most existing models. The day ahead price forecast is beneficial for both power generators and consumers in bidding for electricity prices. � 2020 IEEE. Final 2023-05-29T08:07:28Z 2023-05-29T08:07:28Z 2020 Conference Paper 10.1109/SCOReD50371.2020.9383184 2-s2.0-85103496018 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103496018&doi=10.1109%2fSCOReD50371.2020.9383184&partnerID=40&md5=483e6dbcd72ea9be03f3a606a5b60898 https://irepository.uniten.edu.my/handle/123456789/25230 2020-January 9383184 159 164 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Electric machine theory; Forecasting; Optimization; Support vector machines; Bacterial Foraging Optimization Algorithm (BFOA); Bacterial foraging optimization algorithms; Day-ahead price forecasts; Forecasting accuracy; Least square support vector machines; Multi-stage optimization; Optimization levels; Uncertain condition; Power markets
author2 56602467500
author_facet 56602467500
Intan Azmira W.A.R.
Ahmad A.
Abidin I.Z.
Yap K.S.
Nasir M.N.M.
Upkli W.R.
format Conference Paper
author Intan Azmira W.A.R.
Ahmad A.
Abidin I.Z.
Yap K.S.
Nasir M.N.M.
Upkli W.R.
spellingShingle Intan Azmira W.A.R.
Ahmad A.
Abidin I.Z.
Yap K.S.
Nasir M.N.M.
Upkli W.R.
Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model
author_sort Intan Azmira W.A.R.
title Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model
title_short Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model
title_full Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model
title_fullStr Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model
title_full_unstemmed Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model
title_sort electricity price prediction with support vector machine and bacterial foraging optimization algorithm for day-ahead model
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427809523957760