Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search
Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/10/827 |
| _version_ | 1811184425363308544 |
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| author | Yi Liang Dongxiao Niu Minquan Ye Wei-Chiang Hong |
| author_facet | Yi Liang Dongxiao Niu Minquan Ye Wei-Chiang Hong |
| author_sort | Yi Liang |
| collection | DOAJ |
| description | Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT) and least squares support vector machine (LSSVM), which is optimized by an improved cuckoo search (CS). To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day’s load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system. |
| first_indexed | 2024-04-11T13:13:01Z |
| format | Article |
| id | doaj.art-df61721d10584f648e8bc13081ad1bd4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:13:01Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-df61721d10584f648e8bc13081ad1bd42022-12-22T04:22:33ZengMDPI AGEnergies1996-10732016-10-0191082710.3390/en9100827en9100827Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo SearchYi Liang0Dongxiao Niu1Minquan Ye2Wei-Chiang Hong3School of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Baoding 071003, ChinaDepartment of Information Management, Oriental Institute of Technology, New Taipei 220, TaiwanDue to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT) and least squares support vector machine (LSSVM), which is optimized by an improved cuckoo search (CS). To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day’s load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system.http://www.mdpi.com/1996-1073/9/10/827short-term load forecastingwavelet transformleast squares support vector machinecuckoo searchGauss disturbance |
| spellingShingle | Yi Liang Dongxiao Niu Minquan Ye Wei-Chiang Hong Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search Energies short-term load forecasting wavelet transform least squares support vector machine cuckoo search Gauss disturbance |
| title | Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search |
| title_full | Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search |
| title_fullStr | Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search |
| title_full_unstemmed | Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search |
| title_short | Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search |
| title_sort | short term load forecasting based on wavelet transform and least squares support vector machine optimized by improved cuckoo search |
| topic | short-term load forecasting wavelet transform least squares support vector machine cuckoo search Gauss disturbance |
| url | http://www.mdpi.com/1996-1073/9/10/827 |
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