Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm
Photo-voltaic (PV) is one of the most abundant sources on the earth for the generation of electricity. Although, due to the stochastic nature of PV characteristics to sustain constant power, an accurate PV power prediction is needed for a grid-connected PV system. The proposed model of support vecto...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37280/ http://umpir.ump.edu.my/id/eprint/37280/1/Ultra-short-term%20PV%20power%20forecasting%20based%20on%20a%20support%20vector%20machine%20.pdf http://umpir.ump.edu.my/id/eprint/37280/2/Ultra-short-term%20PV%20power%20forecasting.pdf |
| _version_ | 1848825209309626368 |
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| author | Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Jewaliddin, S. K. Peddakapu, K. Srinivasarao, P. |
| author_facet | Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Jewaliddin, S. K. Peddakapu, K. Srinivasarao, P. |
| author_sort | Kishore, D. J. Krishna |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Photo-voltaic (PV) is one of the most abundant sources on the earth for the generation of electricity. Although, due to the stochastic nature of PV characteristics to sustain constant power, an accurate PV power prediction is needed for a grid-connected PV system. The proposed model of support vector machine (SVM) with improved dragonfly algorithm(IDA) is used to forecast the PV power. Previously, Theexecution can be done by dragonfly algorithm (DA) through adaptive learning factor along with the differential evolution technique. The IDA is used to select the best support vector machine parameters. Eventually, the suggested model provides better performance as compared to the other algorithm such as SVM with dragonfly algorithm(SVM-DA). It is suitable for forecasting ultra-short-term PV power. |
| first_indexed | 2025-11-15T03:25:17Z |
| format | Conference or Workshop Item |
| id | ump-37280 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:25:17Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-372802023-03-14T05:39:06Z http://umpir.ump.edu.my/id/eprint/37280/ Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Jewaliddin, S. K. Peddakapu, K. Srinivasarao, P. TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Photo-voltaic (PV) is one of the most abundant sources on the earth for the generation of electricity. Although, due to the stochastic nature of PV characteristics to sustain constant power, an accurate PV power prediction is needed for a grid-connected PV system. The proposed model of support vector machine (SVM) with improved dragonfly algorithm(IDA) is used to forecast the PV power. Previously, Theexecution can be done by dragonfly algorithm (DA) through adaptive learning factor along with the differential evolution technique. The IDA is used to select the best support vector machine parameters. Eventually, the suggested model provides better performance as compared to the other algorithm such as SVM with dragonfly algorithm(SVM-DA). It is suitable for forecasting ultra-short-term PV power. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37280/1/Ultra-short-term%20PV%20power%20forecasting%20based%20on%20a%20support%20vector%20machine%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/37280/2/Ultra-short-term%20PV%20power%20forecasting.pdf Kishore, D. J. Krishna and Mohamed, M. R. and Sudhakar, K. and Jewaliddin, S. K. and Peddakapu, K. and Srinivasarao, P. (2021) Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm. In: 1st IEEE International Conference on Emerging Trends in Industry 4.0, ETI 4.0 2021 , 19 - 21 May 2021 , Raigarh, India. pp. 1-5. (175124). ISBN 978-166542237-6 (Published) https://doi.org/ 10.1109/ETI4.051663.2021.9619323 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Kishore, D. J. Krishna Mohamed, M. R. Sudhakar, K. Jewaliddin, S. K. Peddakapu, K. Srinivasarao, P. Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm |
| title | Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm |
| title_full | Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm |
| title_fullStr | Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm |
| title_full_unstemmed | Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm |
| title_short | Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm |
| title_sort | ultra-short-term pv power forecasting based on a support vector machine with improved dragonfly algorithm |
| topic | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37280/ http://umpir.ump.edu.my/id/eprint/37280/ http://umpir.ump.edu.my/id/eprint/37280/1/Ultra-short-term%20PV%20power%20forecasting%20based%20on%20a%20support%20vector%20machine%20.pdf http://umpir.ump.edu.my/id/eprint/37280/2/Ultra-short-term%20PV%20power%20forecasting.pdf |