Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta
Due to the depletion of fossil fuels and environmental concerns, renewable energy has become increasingly popular. Even so, the economic competitiveness and cost of energy in renewable systems remain a challenge. Optimization of renewable energy systems from an economic standpoint is important not o...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | Green Finance |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/GF.2024027 |
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| author | Ali Omidkar Razieh Es'haghian Hua Song |
| author_facet | Ali Omidkar Razieh Es'haghian Hua Song |
| author_sort | Ali Omidkar |
| collection | DOAJ |
| description | Due to the depletion of fossil fuels and environmental concerns, renewable energy has become increasingly popular. Even so, the economic competitiveness and cost of energy in renewable systems remain a challenge. Optimization of renewable energy systems from an economic standpoint is important not only from the point of view of researchers but also industry owners, stakeholders, and governments. Solar collectors are one of the most optimized and developed renewable energy systems. However, due to the high degree of nonlinearity and many unknowns associated with these systems, optimizing them is an extremely time-consuming and expensive process. This study presents an economically optimal design platform for solar power plants with a fast response time using machine learning techniques. Compared with traditional mathematical optimization, the speed of economic optimization with the help of the machine learning method increased by up to 1100 times. A total of seven continuous variables and three discrete variables were selected for optimization of the parabolic trough solar collector. The objective functions were to optimize the exergy efficiency and the heat cost. As part of the environmental assessment, the cost of carbon dioxide emission was calculated based on the system's exergy and energy efficiencies. According to the sensitivity analysis, the mass flow of working fluid and the initial temperature of the fluid play the most significant roles. A simulated solar collector in Calgary was optimized in order to evaluate the applicability of the proposed platform. |
| format | Article |
| id | doaj-art-3819d758827b476691c94fd3bdbb2d0b |
| institution | Kabale University |
| issn | 2643-1092 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Green Finance |
| spelling | doaj-art-3819d758827b476691c94fd3bdbb2d0b2025-01-24T01:03:57ZengAIMS PressGreen Finance2643-10922024-12-016469872710.3934/GF.2024027Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, AlbertaAli Omidkar0Razieh Es'haghian1Hua Song2Chemical and Petroleum Engineering Department, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada, T2N 4V8Chemical and Petroleum Engineering Department, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada, T2N 4V8Chemical and Petroleum Engineering Department, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada, T2N 4V8Due to the depletion of fossil fuels and environmental concerns, renewable energy has become increasingly popular. Even so, the economic competitiveness and cost of energy in renewable systems remain a challenge. Optimization of renewable energy systems from an economic standpoint is important not only from the point of view of researchers but also industry owners, stakeholders, and governments. Solar collectors are one of the most optimized and developed renewable energy systems. However, due to the high degree of nonlinearity and many unknowns associated with these systems, optimizing them is an extremely time-consuming and expensive process. This study presents an economically optimal design platform for solar power plants with a fast response time using machine learning techniques. Compared with traditional mathematical optimization, the speed of economic optimization with the help of the machine learning method increased by up to 1100 times. A total of seven continuous variables and three discrete variables were selected for optimization of the parabolic trough solar collector. The objective functions were to optimize the exergy efficiency and the heat cost. As part of the environmental assessment, the cost of carbon dioxide emission was calculated based on the system's exergy and energy efficiencies. According to the sensitivity analysis, the mass flow of working fluid and the initial temperature of the fluid play the most significant roles. A simulated solar collector in Calgary was optimized in order to evaluate the applicability of the proposed platform.https://www.aimspress.com/article/doi/10.3934/GF.2024027machine learning modelfirefly optimization algorithmsolar collector |
| spellingShingle | Ali Omidkar Razieh Es'haghian Hua Song Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta Green Finance machine learning model firefly optimization algorithm solar collector |
| title | Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta |
| title_full | Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta |
| title_fullStr | Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta |
| title_full_unstemmed | Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta |
| title_short | Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta |
| title_sort | developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms case study optimizing solar thermal collector in calgary alberta |
| topic | machine learning model firefly optimization algorithm solar collector |
| url | https://www.aimspress.com/article/doi/10.3934/GF.2024027 |
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