Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universitas Indonesia
2024-01-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/5543 |
| _version_ | 1827375002804027392 |
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| author | Faisul Arif Ahmad Junchen Liu Fazirulhisyam Hashim Khairulmizam Samsudin |
| author_facet | Faisul Arif Ahmad Junchen Liu Fazirulhisyam Hashim Khairulmizam Samsudin |
| author_sort | Faisul Arif Ahmad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:43:22Z |
| format | Article |
| id | doaj.art-446f4d53e2c24ddb8dd1268d0be8b6cd |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-03-08T11:43:22Z |
| publishDate | 2024-01-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-446f4d53e2c24ddb8dd1268d0be8b6cd2024-01-25T01:56:09ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002024-01-0115112112910.14716/ijtech.v15i1.55435543Short-Term Load Forecasting Utilizing a Combination Model: A Brief ReviewFaisul Arif Ahmad0Junchen Liu1Fazirulhisyam Hashim2Khairulmizam Samsudin3Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Seri Kembangan 43400, MalaysiaDepartment of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Seri Kembangan 43400, MalaysiaDepartment of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Seri Kembangan 43400, MalaysiaDepartment of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Seri Kembangan 43400, MalaysiaTo 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.https://ijtech.eng.ui.ac.id/article/view/5543combined modellstmparticle swarm optimizationstlf |
| spellingShingle | Faisul Arif Ahmad Junchen Liu Fazirulhisyam Hashim Khairulmizam Samsudin Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review International Journal of Technology combined model lstm particle swarm optimization stlf |
| title | Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review |
| title_full | Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review |
| title_fullStr | Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review |
| title_full_unstemmed | Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review |
| title_short | Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review |
| title_sort | short term load forecasting utilizing a combination model a brief review |
| topic | combined model lstm particle swarm optimization stlf |
| url | https://ijtech.eng.ui.ac.id/article/view/5543 |
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