Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model
In this paper, we develop an optimal placement of solar-wind energy systems using restricted Boltzmann machine (RBM). The RBM considers various factors to scale the process of optimal placement and enables proper sizing and placement for attaining increased electricity production from both wind and...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/2881603 |
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| _version_ | 1832553661612425216 |
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| author | Sundeep Siddula G. K. Prashanth Praful Nandankar Ram Subbiah Saikh Mohammad Wabaidur Essam A. Al-Ammar M. H. Siddique Subash Thanappan |
| author_facet | Sundeep Siddula G. K. Prashanth Praful Nandankar Ram Subbiah Saikh Mohammad Wabaidur Essam A. Al-Ammar M. H. Siddique Subash Thanappan |
| author_sort | Sundeep Siddula |
| collection | DOAJ |
| description | In this paper, we develop an optimal placement of solar-wind energy systems using restricted Boltzmann machine (RBM). The RBM considers various factors to scale the process of optimal placement and enables proper sizing and placement for attaining increased electricity production from both wind and solar systems. The multiobjective criterion from both solar and wind energy farms simulated on MATLAB simulator shows an increased number of accuracies with reduced mean average error and computation time during training and testing. The results show that the RBM achieves improved rate of finding the optimal placement with a lesser cost and computation time of lesser than 2 ms than other methods. |
| format | Article |
| id | doaj-art-12f3771e8a3f426a88f7ef73b813d1f7 |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-12f3771e8a3f426a88f7ef73b813d1f72025-02-03T05:53:33ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/2881603Optimal Placement of Hybrid Wind-Solar System Using Deep Learning ModelSundeep Siddula0G. K. Prashanth1Praful Nandankar2Ram Subbiah3Saikh Mohammad Wabaidur4Essam A. Al-Ammar5M. H. Siddique6Subash Thanappan7Department of Electrical and Electronics EngineeringDepartment of Master of Computer ApplicationsDepartment of Electrical EngineeringDepartment of Mechanical EngineeringChemistry DepartmentDepartment of Electrical EngineeringIntelligent Construction Automation CentreDepartment of Civil EngineeringIn this paper, we develop an optimal placement of solar-wind energy systems using restricted Boltzmann machine (RBM). The RBM considers various factors to scale the process of optimal placement and enables proper sizing and placement for attaining increased electricity production from both wind and solar systems. The multiobjective criterion from both solar and wind energy farms simulated on MATLAB simulator shows an increased number of accuracies with reduced mean average error and computation time during training and testing. The results show that the RBM achieves improved rate of finding the optimal placement with a lesser cost and computation time of lesser than 2 ms than other methods.http://dx.doi.org/10.1155/2022/2881603 |
| spellingShingle | Sundeep Siddula G. K. Prashanth Praful Nandankar Ram Subbiah Saikh Mohammad Wabaidur Essam A. Al-Ammar M. H. Siddique Subash Thanappan Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model International Journal of Photoenergy |
| title | Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model |
| title_full | Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model |
| title_fullStr | Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model |
| title_full_unstemmed | Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model |
| title_short | Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model |
| title_sort | optimal placement of hybrid wind solar system using deep learning model |
| url | http://dx.doi.org/10.1155/2022/2881603 |
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