Cross-substation short term load forecasting using support vector machine
This paper investigates the behavior of a short term load forecasting system in the cross-substation scheme. The proposed forecasting system is based on the support vector machine with the input features of past loads and temperature. It is trained with the data from one substation and tested on the...
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th-cmuir.6653943832-602942018-09-10T03:42:15Z Cross-substation short term load forecasting using support vector machine Jonglak Pahasa Nipon Theera-Umpon Computer Science Engineering This paper investigates the behavior of a short term load forecasting system in the cross-substation scheme. The proposed forecasting system is based on the support vector machine with the input features of past loads and temperature. It is trained with the data from one substation and tested on the blind-test data from other substations. A set of real-world data from 4 substations in Bangkok, i.e., Bangkok Noi, North Bangkok, South Thonburl and Rangsit, is used in the experiments. The results show that the similarities of the daily load's amplitude ranges and patterns of the training substations and the test substations is required to perform the cross-substation forecasting. This observation is beneficial to the model development in that the retraining stage at a new substation may be omitted if the similarities are obeyed. © 2008 IEEE. 2018-09-10T03:40:40Z 2018-09-10T03:40:40Z 2008-10-06 Conference Proceeding 2-s2.0-52949095023 10.1109/ECTICON.2008.4600589 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=52949095023&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60294 |
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Computer Science Engineering Jonglak Pahasa Nipon Theera-Umpon Cross-substation short term load forecasting using support vector machine |
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This paper investigates the behavior of a short term load forecasting system in the cross-substation scheme. The proposed forecasting system is based on the support vector machine with the input features of past loads and temperature. It is trained with the data from one substation and tested on the blind-test data from other substations. A set of real-world data from 4 substations in Bangkok, i.e., Bangkok Noi, North Bangkok, South Thonburl and Rangsit, is used in the experiments. The results show that the similarities of the daily load's amplitude ranges and patterns of the training substations and the test substations is required to perform the cross-substation forecasting. This observation is beneficial to the model development in that the retraining stage at a new substation may be omitted if the similarities are obeyed. © 2008 IEEE. |
| format |
Conference Proceeding |
| author |
Jonglak Pahasa Nipon Theera-Umpon |
| author_facet |
Jonglak Pahasa Nipon Theera-Umpon |
| author_sort |
Jonglak Pahasa |
| title |
Cross-substation short term load forecasting using support vector machine |
| title_short |
Cross-substation short term load forecasting using support vector machine |
| title_full |
Cross-substation short term load forecasting using support vector machine |
| title_fullStr |
Cross-substation short term load forecasting using support vector machine |
| title_full_unstemmed |
Cross-substation short term load forecasting using support vector machine |
| title_sort |
cross-substation short term load forecasting using support vector machine |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=52949095023&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60294 |
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