Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater poten...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/10/12/2180 |
| _version_ | 1811306344222818304 |
|---|---|
| author | Ming-Wei Li Jing Geng Shumei Wang Wei-Chiang Hong |
| author_facet | Ming-Wei Li Jing Geng Shumei Wang Wei-Chiang Hong |
| author_sort | Ming-Wei Li |
| collection | DOAJ |
| description | Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy. |
| first_indexed | 2024-04-13T08:42:44Z |
| format | Article |
| id | doaj.art-df253578b07642d8985e37c151ea23f7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:42:44Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-df253578b07642d8985e37c151ea23f72022-12-22T02:53:50ZengMDPI AGEnergies1996-10732017-12-011012218010.3390/en10122180en10122180Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load ForecastingMing-Wei Li0Jing Geng1Shumei Wang2Wei-Chiang Hong3College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, ChinaSchool of Education Intelligent Technology, Jiangsu Normal University/101, Shanghai Rd., Tongshan District, Xuzhou 221116, Jiangsu, ChinaSchool of Education Intelligent Technology, Jiangsu Normal University/101, Shanghai Rd., Tongshan District, Xuzhou 221116, Jiangsu, ChinaHybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy.https://www.mdpi.com/1996-1073/10/12/2180support vector regressionchaos theoryquantum behaviorbat algorithm (BA)load forecasting |
| spellingShingle | Ming-Wei Li Jing Geng Shumei Wang Wei-Chiang Hong Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting Energies support vector regression chaos theory quantum behavior bat algorithm (BA) load forecasting |
| title | Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting |
| title_full | Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting |
| title_fullStr | Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting |
| title_full_unstemmed | Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting |
| title_short | Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting |
| title_sort | hybrid chaotic quantum bat algorithm with svr in electric load forecasting |
| topic | support vector regression chaos theory quantum behavior bat algorithm (BA) load forecasting |
| url | https://www.mdpi.com/1996-1073/10/12/2180 |
| work_keys_str_mv | AT mingweili hybridchaoticquantumbatalgorithmwithsvrinelectricloadforecasting AT jinggeng hybridchaoticquantumbatalgorithmwithsvrinelectricloadforecasting AT shumeiwang hybridchaoticquantumbatalgorithmwithsvrinelectricloadforecasting AT weichianghong hybridchaoticquantumbatalgorithmwithsvrinelectricloadforecasting |