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.
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Hybridization solution of electrical energy demand response and forecasting program by using PSO-LSSVM technique.pdf Restricted to Repository staff only Download (677kB) |
Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | ETOU tariff, Forecasting, Least square support vector machine, Particle swarm optimization |
| Divisions: | Faculty Of Electrical Technology And Engineering |
| Depositing User: | Maizatul Najwa Ahmad |
| Date Deposited: | 10 Oct 2024 10:21 |
| Last Modified: | 10 Oct 2024 10:21 |
| URI: | http://eprints.utem.edu.my/id/eprint/27946 |
| Statistic Details: | View Download Statistic |
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