Citation
Abstract
To deliver electricity to customers safely and economically, power companies encounter numerous economic and technical challenges in their operations. Power flow analysis, planning, and control of power systems stand out among these issues. Over the last several years, one of the most developing study topics in this vital and demanding discipline has been electricity short-term load forecasting (STLF). Power system dispatching, emergency analysis, power flow analysis, planning, and maintenance all require it. This study emphasizes new research on long short-term memory (LSTM) algorithms related to particle swarm optimization (PSO) inside this area of short-term load forecasting. The paper presents an in-depth overview of hybrid networks that combine LSTM and PSO and have been effectively used for STLF. In the future, the integration of LSTM and PSO in the development of comprehensive prediction methods and techniques for multi-heterogeneous models is expected to offer significant opportunities. With an increased dataset, the utilization of advanced multi-models for comprehensive power load prediction is anticipated to achieve higher accuracy.
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Official URL or Download Paper: https://ijtech.eng.ui.ac.id/article/view/5543
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Engineering |
DOI Number: | https://doi.org/10.14716/ijtech.v15i1.5543 |
Publisher: | Faculty of Engineering, Universitas Indonesia |
Keywords: | Combined model; LSTM; Particle swarm optimization; STLF; Short-term load forecasting; Power systems; Energy efficiency; Forecast accuracy; Hybrid networks; Electricity demand; Optimization techniques |
Depositing User: | Mr. Mohamad Syahrul Nizam Md Ishak |
Date Deposited: | 11 May 2024 14:59 |
Last Modified: | 11 May 2024 14:59 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.14716/ijtech.v15i1.5543 |
URI: | http://psasir.upm.edu.my/id/eprint/106244 |
Statistic Details: | View Download Statistic |
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