A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection
In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to ge...
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| Published in | IEEE transactions on power systems Vol. 31; no. 3; pp. 1788 - 1798 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.05.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the Levenberg-Marquardt method is proposed to improve the learning accuracy of neural networks. Third, a feature selection method based on the conditional mutual information is developed to select a compact set of input variables for the forecasting model. Fourth, to realize an accurate ensemble forecast, partial least squares regression is utilized as a combining approach to aggregate the individual forecasts. Numerical testing shows that proposed method can obtain better forecasting results in comparison with other standard and state-of-the-art methods. |
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| AbstractList | In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the Levenberg-Marquardt method is proposed to improve the learning accuracy of neural networks. Third, a feature selection method based on the conditional mutual information is developed to select a compact set of input variables for the forecasting model. Fourth, to realize an accurate ensemble forecast, partial least squares regression is utilized as a combining approach to aggregate the individual forecasts. Numerical testing shows that proposed method can obtain better forecasting results in comparison with other standard and state-of-the-art methods. |
| Author | Peng Wang Song Li Goel, Lalit |
| Author_xml | – sequence: 1 surname: Song Li fullname: Song Li email: sli5@e.ntu.edu.sg organization: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore – sequence: 2 surname: Peng Wang fullname: Peng Wang email: epwang@ntu.edu.sg organization: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore – sequence: 3 givenname: Lalit surname: Goel fullname: Goel, Lalit email: elkgoel@ntu.edu.sg organization: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore |
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| SubjectTerms | Accuracy Ensemble method extreme learning machine Forecasting Forecasting techniques load forecast Load forecasting Neural networks partial least-squares regression Training wavelet transform Wavelet transforms |
| Title | A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection |
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