Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction
Article; case study; genetic algorithm; mathematical computing; process optimization; sensitivity analysis; solar radiation; statistical model; statistical parameters; support vector machine; algorithm; forecasting; human; humidity; regression analysis; solar energy; sunlight; turkey (bird); wind; A...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Public Library of Science
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tenaga Nasional |
| id |
my.uniten.dspace-24680 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-246802023-05-29T15:25:48Z Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction Ghazvinian H. Mousavi S.-F. Karami H. Farzin S. Ehteram M. Hossain M.S. Fai C.M. Hashim H.B. Singh V.P. Ros F.C. Ahmed A.N. Afan H.A. Lai S.H. El-Shafie A. 57209110288 7003344568 36863982200 55315758000 57113510800 55579596900 57214146115 56800153400 57211219633 57222964772 57214837520 56436626600 36102664300 16068189400 Article; case study; genetic algorithm; mathematical computing; process optimization; sensitivity analysis; solar radiation; statistical model; statistical parameters; support vector machine; algorithm; forecasting; human; humidity; regression analysis; solar energy; sunlight; turkey (bird); wind; Algorithms; Forecasting; Humans; Humidity; Regression Analysis; Solar Energy; Sunlight; Support Vector Machine; Turkey; Wind Solar energy is a major type of renewable energy, and its estimation is important for decision- makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. � 2019 Ghazvinian et al. Final 2023-05-29T07:25:48Z 2023-05-29T07:25:48Z 2019 Article 10.1371/journal.pone.0217634 2-s2.0-85066505795 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066505795&doi=10.1371%2fjournal.pone.0217634&partnerID=40&md5=261a81c04082f7656b172db8fbb7804c https://irepository.uniten.edu.my/handle/123456789/24680 14 5 e0217634 All Open Access, Gold, Green Public Library of Science Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
Article; case study; genetic algorithm; mathematical computing; process optimization; sensitivity analysis; solar radiation; statistical model; statistical parameters; support vector machine; algorithm; forecasting; human; humidity; regression analysis; solar energy; sunlight; turkey (bird); wind; Algorithms; Forecasting; Humans; Humidity; Regression Analysis; Solar Energy; Sunlight; Support Vector Machine; Turkey; Wind |
| author2 |
57209110288 |
| author_facet |
57209110288 Ghazvinian H. Mousavi S.-F. Karami H. Farzin S. Ehteram M. Hossain M.S. Fai C.M. Hashim H.B. Singh V.P. Ros F.C. Ahmed A.N. Afan H.A. Lai S.H. El-Shafie A. |
| format |
Article |
| author |
Ghazvinian H. Mousavi S.-F. Karami H. Farzin S. Ehteram M. Hossain M.S. Fai C.M. Hashim H.B. Singh V.P. Ros F.C. Ahmed A.N. Afan H.A. Lai S.H. El-Shafie A. |
| spellingShingle |
Ghazvinian H. Mousavi S.-F. Karami H. Farzin S. Ehteram M. Hossain M.S. Fai C.M. Hashim H.B. Singh V.P. Ros F.C. Ahmed A.N. Afan H.A. Lai S.H. El-Shafie A. Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction |
| author_sort |
Ghazvinian H. |
| title |
Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction |
| title_short |
Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction |
| title_full |
Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction |
| title_fullStr |
Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction |
| title_full_unstemmed |
Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction |
| title_sort |
integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction |
| publisher |
Public Library of Science |
| publishDate |
2023 |
| _version_ |
1806428435548995584 |