Long-term load forecasting using grey wolf optimizer-least-squares support vector machine
Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Institute of Advanced Engineering and Science
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-25279 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-252792023-05-29T16:07:49Z Long-term load forecasting using grey wolf optimizer-least-squares support vector machine Yasin Z.M. Salim N.A. Aziz N.F.A. Ali Y.M. Mohamad H. 57211410254 36806685300 57221906825 57215422906 36809989400 Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-LSSVM. Mean Absolute Percentage Error (MAPE) is used as the quantify measurement of the prediction model. The objective of the optimization is to minimize the value of MAPE. The performance of GWO-LSSVM is compared with other methods such as LSSVM and Ant Lion Optimizer � Least-Square Support Vector Machine (ALO-LSSVM). From the results obtained, it can be concluded that GWO-LSSVM provide lower MAPE value which is 0.13% as compared to other methods. � 2020, Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T08:07:49Z 2023-05-29T08:07:49Z 2020 Article 10.11591/ijai.v9.i3.pp417-423 2-s2.0-85086913660 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086913660&doi=10.11591%2fijai.v9.i3.pp417-423&partnerID=40&md5=440b480ce687fa70f19792f424045c85 https://irepository.uniten.edu.my/handle/123456789/25279 9 3 417 423 All Open Access, Gold, Green Institute of Advanced Engineering and Science 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 |
Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-LSSVM. Mean Absolute Percentage Error (MAPE) is used as the quantify measurement of the prediction model. The objective of the optimization is to minimize the value of MAPE. The performance of GWO-LSSVM is compared with other methods such as LSSVM and Ant Lion Optimizer � Least-Square Support Vector Machine (ALO-LSSVM). From the results obtained, it can be concluded that GWO-LSSVM provide lower MAPE value which is 0.13% as compared to other methods. � 2020, Institute of Advanced Engineering and Science. All rights reserved. |
| author2 |
57211410254 |
| author_facet |
57211410254 Yasin Z.M. Salim N.A. Aziz N.F.A. Ali Y.M. Mohamad H. |
| format |
Article |
| author |
Yasin Z.M. Salim N.A. Aziz N.F.A. Ali Y.M. Mohamad H. |
| spellingShingle |
Yasin Z.M. Salim N.A. Aziz N.F.A. Ali Y.M. Mohamad H. Long-term load forecasting using grey wolf optimizer-least-squares support vector machine |
| author_sort |
Yasin Z.M. |
| title |
Long-term load forecasting using grey wolf optimizer-least-squares support vector machine |
| title_short |
Long-term load forecasting using grey wolf optimizer-least-squares support vector machine |
| title_full |
Long-term load forecasting using grey wolf optimizer-least-squares support vector machine |
| title_fullStr |
Long-term load forecasting using grey wolf optimizer-least-squares support vector machine |
| title_full_unstemmed |
Long-term load forecasting using grey wolf optimizer-least-squares support vector machine |
| title_sort |
long-term load forecasting using grey wolf optimizer-least-squares support vector machine |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2023 |
| _version_ |
1806425860705615872 |
| score |
13.222552 |