A quadratic $$\nu $$ ν -support vector regression approach for load forecasting
Abstract This article focuses on electric load forecasting, which is a challenging task in the energy industry. In this paper, a novel kernel-free $$\nu $$ ν -support vector regression model is proposed for electric load forecasting. The proposed model produces a reduced quadratic surface for nonlin...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01730-7 |
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| _version_ | 1832571181839941632 |
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| author | Yanhe Jia Shuaiguang Zhou Yiwen Wang Fengming Lin Zheming Gao |
| author_facet | Yanhe Jia Shuaiguang Zhou Yiwen Wang Fengming Lin Zheming Gao |
| author_sort | Yanhe Jia |
| collection | DOAJ |
| description | Abstract This article focuses on electric load forecasting, which is a challenging task in the energy industry. In this paper, a novel kernel-free $$\nu $$ ν -support vector regression model is proposed for electric load forecasting. The proposed model produces a reduced quadratic surface for nonlinear regression. A feature weighting strategy is adopted to estimate the relevance of the features in the load history. To reduce the effects of outliers in the load history, a weight is assigned to represent the relative importance of each data point. Some computational experiments are conducted on some public benchmark data sets to show the superior performance of the proposed model over some widely used regression models. The results of some extensive computational experiments on the electric load data from the Global Energy Forecasting Competition 2012 and the ISO New England demonstrate better average accuracy of the proposed model. |
| format | Article |
| id | doaj-art-8e554d0d97584646af8d64a7265ea904 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-8e554d0d97584646af8d64a7265ea9042025-02-02T12:49:07ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111111210.1007/s40747-024-01730-7A quadratic $$\nu $$ ν -support vector regression approach for load forecastingYanhe Jia0Shuaiguang Zhou1Yiwen Wang2Fengming Lin3Zheming Gao4College of Management Science and Engineering, Beijing Information Science and Technology UniversityCollege of Information Science and Engineering, Northeastern UniversityCollege of Information Science and Engineering, Northeastern UniversityEnergy Production and Infrastructure Center (EPIC) in The William States Lee College of Engineering, University of North Carolina at CharlotteCollege of Information Science and Engineering, Northeastern UniversityAbstract This article focuses on electric load forecasting, which is a challenging task in the energy industry. In this paper, a novel kernel-free $$\nu $$ ν -support vector regression model is proposed for electric load forecasting. The proposed model produces a reduced quadratic surface for nonlinear regression. A feature weighting strategy is adopted to estimate the relevance of the features in the load history. To reduce the effects of outliers in the load history, a weight is assigned to represent the relative importance of each data point. Some computational experiments are conducted on some public benchmark data sets to show the superior performance of the proposed model over some widely used regression models. The results of some extensive computational experiments on the electric load data from the Global Energy Forecasting Competition 2012 and the ISO New England demonstrate better average accuracy of the proposed model.https://doi.org/10.1007/s40747-024-01730-7Kernel-free support vector regressionElectric load forecastingMachine learningWeighted support vector regressionFeature weighting |
| spellingShingle | Yanhe Jia Shuaiguang Zhou Yiwen Wang Fengming Lin Zheming Gao A quadratic $$\nu $$ ν -support vector regression approach for load forecasting Complex & Intelligent Systems Kernel-free support vector regression Electric load forecasting Machine learning Weighted support vector regression Feature weighting |
| title | A quadratic $$\nu $$ ν -support vector regression approach for load forecasting |
| title_full | A quadratic $$\nu $$ ν -support vector regression approach for load forecasting |
| title_fullStr | A quadratic $$\nu $$ ν -support vector regression approach for load forecasting |
| title_full_unstemmed | A quadratic $$\nu $$ ν -support vector regression approach for load forecasting |
| title_short | A quadratic $$\nu $$ ν -support vector regression approach for load forecasting |
| title_sort | quadratic nu ν support vector regression approach for load forecasting |
| topic | Kernel-free support vector regression Electric load forecasting Machine learning Weighted support vector regression Feature weighting |
| url | https://doi.org/10.1007/s40747-024-01730-7 |
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