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Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique

The ever-growing electricity market provides an excellent opportunity for the industrial sector to implement effective energy management through demand response (DR). The demand for poultry meat and eggs is expected to continue increasing with the growing population, leading to higher energy generat...

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Main Authors: Ali, Amira Noor Farhanie, Sulaima, Mohamad Fani, Tahir, Musthafah Mohd., Razak, Intan Azmira Wan Abdul, Kadir, Aida Fazliana Abdul, Rahman, Zulkifli Ab.
Format: Conference or Workshop Item
Language:English
Published: 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27946/1/Hybridization%20solution%20of%20electrical%20energy%20demand%20response%20and%20forecasting%20program%20by%20using%20PSO-LSSVM%20technique.pdf
http://eprints.utem.edu.my/id/eprint/27946/
https://sciprofiles.com/publication/view/ef693db4a272834558654fdf18b521a3
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spelling my.utem.eprints.279462024-10-10T10:21:47Z http://eprints.utem.edu.my/id/eprint/27946/ Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique Ali, Amira Noor Farhanie Sulaima, Mohamad Fani Tahir, Musthafah Mohd. Razak, Intan Azmira Wan Abdul Kadir, Aida Fazliana Abdul Rahman, Zulkifli Ab. The ever-growing electricity market provides an excellent opportunity for the industrial sector to implement effective energy management through demand response (DR). The demand for poultry meat and eggs is expected to continue increasing with the growing population, leading to higher energy generation costs during peak periods. To overcome this challenge, a demand-side management (DSM) approach is put into action, which involves the use of DR schemes and diverse action strategies. The suggested study will optimize energy savings in the industrial sector and improve the sector's power consumption profile. The study uses a particle swarm optimization (PSO) technique and a least square support vector machine (LSSVM) to forecast short-term load and optimize demand profiles under the Enhance Time of Use (ETOU) tariff scheme. The proposed formulation of the ETOU optimization achieves an energy cost savings of up to 7.57% (PSO) and 7.98% (PSO-LSSVM), and the proposed models are intended to lower the cost of electrical energy usage across all price ranges. The study's findings will assist manufacturers in transitioning to the ETOU tariff and contribute to the national DSM initiative program. Future research may examine other optimization algorithms and load forecasting models to refine ETOU tariff rate price reduction strategies and define available load for specific load management strategies. 2023 Conference or Workshop Item PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27946/1/Hybridization%20solution%20of%20electrical%20energy%20demand%20response%20and%20forecasting%20program%20by%20using%20PSO-LSSVM%20technique.pdf Ali, Amira Noor Farhanie and Sulaima, Mohamad Fani and Tahir, Musthafah Mohd. and Razak, Intan Azmira Wan Abdul and Kadir, Aida Fazliana Abdul and Rahman, Zulkifli Ab. (2023) Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique. In: 2nd International Conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2023, 2 August 2023through 3 August 2023, Banda Aceh. https://sciprofiles.com/publication/view/ef693db4a272834558654fdf18b521a3
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description The ever-growing electricity market provides an excellent opportunity for the industrial sector to implement effective energy management through demand response (DR). The demand for poultry meat and eggs is expected to continue increasing with the growing population, leading to higher energy generation costs during peak periods. To overcome this challenge, a demand-side management (DSM) approach is put into action, which involves the use of DR schemes and diverse action strategies. The suggested study will optimize energy savings in the industrial sector and improve the sector's power consumption profile. The study uses a particle swarm optimization (PSO) technique and a least square support vector machine (LSSVM) to forecast short-term load and optimize demand profiles under the Enhance Time of Use (ETOU) tariff scheme. The proposed formulation of the ETOU optimization achieves an energy cost savings of up to 7.57% (PSO) and 7.98% (PSO-LSSVM), and the proposed models are intended to lower the cost of electrical energy usage across all price ranges. The study's findings will assist manufacturers in transitioning to the ETOU tariff and contribute to the national DSM initiative program. Future research may examine other optimization algorithms and load forecasting models to refine ETOU tariff rate price reduction strategies and define available load for specific load management strategies.
format Conference or Workshop Item
author Ali, Amira Noor Farhanie
Sulaima, Mohamad Fani
Tahir, Musthafah Mohd.
Razak, Intan Azmira Wan Abdul
Kadir, Aida Fazliana Abdul
Rahman, Zulkifli Ab.
spellingShingle Ali, Amira Noor Farhanie
Sulaima, Mohamad Fani
Tahir, Musthafah Mohd.
Razak, Intan Azmira Wan Abdul
Kadir, Aida Fazliana Abdul
Rahman, Zulkifli Ab.
Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique
author_facet Ali, Amira Noor Farhanie
Sulaima, Mohamad Fani
Tahir, Musthafah Mohd.
Razak, Intan Azmira Wan Abdul
Kadir, Aida Fazliana Abdul
Rahman, Zulkifli Ab.
author_sort Ali, Amira Noor Farhanie
title Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique
title_short Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique
title_full Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique
title_fullStr Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique
title_full_unstemmed Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique
title_sort hybridization solution of electrical energy demand response and forecasting program by using pso-lssvm technique
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27946/1/Hybridization%20solution%20of%20electrical%20energy%20demand%20response%20and%20forecasting%20program%20by%20using%20PSO-LSSVM%20technique.pdf
http://eprints.utem.edu.my/id/eprint/27946/
https://sciprofiles.com/publication/view/ef693db4a272834558654fdf18b521a3
_version_ 1814061437453074432
score 13.211869

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