Dual possibilistic regression models of support vector machines and application in power load forecasting
Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720921636 |
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| _version_ | 1849435115017469952 |
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| author | Xianfei Yang Xiang Yu Hui Lu |
| author_facet | Xianfei Yang Xiang Yu Hui Lu |
| author_sort | Xianfei Yang |
| collection | DOAJ |
| description | Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset. |
| format | Article |
| id | doaj-art-5c9244ccc4de4bfb9789f61ba6055d23 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5c9244ccc4de4bfb9789f61ba6055d232025-08-20T03:26:25ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-05-011610.1177/1550147720921636Dual possibilistic regression models of support vector machines and application in power load forecastingXianfei Yang0Xiang Yu1Hui Lu2School of Electronics and Information Engineering, Taizhou University, Taizhou, ChinaSchool of Electronics and Information Engineering, Taizhou University, Taizhou, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, ChinaPower load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.https://doi.org/10.1177/1550147720921636 |
| spellingShingle | Xianfei Yang Xiang Yu Hui Lu Dual possibilistic regression models of support vector machines and application in power load forecasting International Journal of Distributed Sensor Networks |
| title | Dual possibilistic regression models of support vector machines and application in power load forecasting |
| title_full | Dual possibilistic regression models of support vector machines and application in power load forecasting |
| title_fullStr | Dual possibilistic regression models of support vector machines and application in power load forecasting |
| title_full_unstemmed | Dual possibilistic regression models of support vector machines and application in power load forecasting |
| title_short | Dual possibilistic regression models of support vector machines and application in power load forecasting |
| title_sort | dual possibilistic regression models of support vector machines and application in power load forecasting |
| url | https://doi.org/10.1177/1550147720921636 |
| work_keys_str_mv | AT xianfeiyang dualpossibilisticregressionmodelsofsupportvectormachinesandapplicationinpowerloadforecasting AT xiangyu dualpossibilisticregressionmodelsofsupportvectormachinesandapplicationinpowerloadforecasting AT huilu dualpossibilisticregressionmodelsofsupportvectormachinesandapplicationinpowerloadforecasting |