An accurate medium-term load forecasting based on hybrid technique
An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load d...
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Institute of Advanced Engineering and Science
2023
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| author | Yasin Z.M. Aziz N.F.A. Salim N.A. Wahab N.A. Rahmat N.A. |
| author2 | 57211410254 |
| author_facet | 57211410254 Yasin Z.M. Aziz N.F.A. Salim N.A. Wahab N.A. Rahmat N.A. |
| author_sort | Yasin Z.M. |
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| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM. � 2018 Institute of Advanced Engineering and Science All rights reserved. |
| format | Article |
| id | my.uniten.dspace-23674 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | my.uniten.dspace-236742023-05-29T14:50:57Z An accurate medium-term load forecasting based on hybrid technique Yasin Z.M. Aziz N.F.A. Salim N.A. Wahab N.A. Rahmat N.A. 57211410254 57221906825 36806685300 35790572400 55647163881 An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM. � 2018 Institute of Advanced Engineering and Science All rights reserved. Final 2023-05-29T06:50:56Z 2023-05-29T06:50:56Z 2018 Article 10.11591/ijeecs.v12.i1.pp161-167 2-s2.0-85051280257 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051280257&doi=10.11591%2fijeecs.v12.i1.pp161-167&partnerID=40&md5=549c114e0048986ccf15fa291a7de7f0 https://irepository.uniten.edu.my/handle/123456789/23674 12 1 161 167 All Open Access, Green Institute of Advanced Engineering and Science Scopus |
| spellingShingle | Yasin Z.M. Aziz N.F.A. Salim N.A. Wahab N.A. Rahmat N.A. An accurate medium-term load forecasting based on hybrid technique |
| title | An accurate medium-term load forecasting based on hybrid technique |
| title_full | An accurate medium-term load forecasting based on hybrid technique |
| title_fullStr | An accurate medium-term load forecasting based on hybrid technique |
| title_full_unstemmed | An accurate medium-term load forecasting based on hybrid technique |
| title_short | An accurate medium-term load forecasting based on hybrid technique |
| title_sort | accurate medium-term load forecasting based on hybrid technique |
| url_provider | http://dspace.uniten.edu.my/ |