Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid
In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogri...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/23/8933 |
| _version_ | 1797463371552915456 |
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| author | Asad Ali Muhammad Salman Fakhar Syed Abdul Rahman Kashif Ghulam Abbas Irfan Ahmad Khan Akhtar Rasool Nasim Ullah |
| author_facet | Asad Ali Muhammad Salman Fakhar Syed Abdul Rahman Kashif Ghulam Abbas Irfan Ahmad Khan Akhtar Rasool Nasim Ullah |
| author_sort | Asad Ali |
| collection | DOAJ |
| description | In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper. |
| first_indexed | 2024-03-09T17:49:42Z |
| format | Article |
| id | doaj.art-b2f5d154d40c486eaae83b273c6cc5c0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T17:49:42Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b2f5d154d40c486eaae83b273c6cc5c02023-11-24T10:52:36ZengMDPI AGEnergies1996-10732022-11-011523893310.3390/en15238933Optimal Scheduling of Neural Network-Based Estimated Renewable Energy NanogridAsad Ali0Muhammad Salman Fakhar1Syed Abdul Rahman Kashif2Ghulam Abbas3Irfan Ahmad Khan4Akhtar Rasool5Nasim Ullah6Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Electrical Engineering, The University of Lahore, Lahore 54000, PakistanClean and Resilient Energy Systems (CARES) Lab, Electrical and Computer Engineering Department, Texas A&M University, Galveston, TX 77553, USADepartment of Electrical Engineering, University of Botswana, Gaborone, BotswanaDepartment of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi ArabiaIn developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper.https://www.mdpi.com/1996-1073/15/23/8933renewable energy resourcesneural networksoptimization techniqueswind energy conversion systemforecastingnanogrid |
| spellingShingle | Asad Ali Muhammad Salman Fakhar Syed Abdul Rahman Kashif Ghulam Abbas Irfan Ahmad Khan Akhtar Rasool Nasim Ullah Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid Energies renewable energy resources neural networks optimization techniques wind energy conversion system forecasting nanogrid |
| title | Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid |
| title_full | Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid |
| title_fullStr | Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid |
| title_full_unstemmed | Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid |
| title_short | Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid |
| title_sort | optimal scheduling of neural network based estimated renewable energy nanogrid |
| topic | renewable energy resources neural networks optimization techniques wind energy conversion system forecasting nanogrid |
| url | https://www.mdpi.com/1996-1073/15/23/8933 |
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