Using machine learning methods for long-term technical and economic evaluation of wind power plants
The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy need...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Green Energy and Resources |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949720525000025 |
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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 | The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable. |
| format | Article |
| id | doaj-art-efa6db2cd0ea4925b325b857c4530671 |
| institution | OA Journals |
| issn | 2949-7205 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Green Energy and Resources |
| spelling | doaj-art-efa6db2cd0ea4925b325b857c45306712025-08-20T02:09:59ZengElsevierGreen Energy and Resources2949-72052025-03-013110011510.1016/j.gerr.2025.100115Using machine learning methods for long-term technical and economic evaluation of wind power plantsAli Omidkar0Razieh Es'haghian1Hua Song2Chemical and Petroleum Engineering Department, Schulich School of Engineering, University of Calgary, Calgary, AB, T2N 4V8, CanadaChemical and Petroleum Engineering Department, Schulich School of Engineering, University of Calgary, Calgary, AB, T2N 4V8, CanadaCorresponding author.; Chemical and Petroleum Engineering Department, Schulich School of Engineering, University of Calgary, Calgary, AB, T2N 4V8, CanadaThe depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable.http://www.sciencedirect.com/science/article/pii/S2949720525000025Renewable energyArtificial neural networkLevelized cost of energy |
| spellingShingle | Ali Omidkar Razieh Es'haghian Hua Song Using machine learning methods for long-term technical and economic evaluation of wind power plants Green Energy and Resources Renewable energy Artificial neural network Levelized cost of energy |
| title | Using machine learning methods for long-term technical and economic evaluation of wind power plants |
| title_full | Using machine learning methods for long-term technical and economic evaluation of wind power plants |
| title_fullStr | Using machine learning methods for long-term technical and economic evaluation of wind power plants |
| title_full_unstemmed | Using machine learning methods for long-term technical and economic evaluation of wind power plants |
| title_short | Using machine learning methods for long-term technical and economic evaluation of wind power plants |
| title_sort | using machine learning methods for long term technical and economic evaluation of wind power plants |
| topic | Renewable energy Artificial neural network Levelized cost of energy |
| url | http://www.sciencedirect.com/science/article/pii/S2949720525000025 |
| work_keys_str_mv | AT aliomidkar usingmachinelearningmethodsforlongtermtechnicalandeconomicevaluationofwindpowerplants AT razieheshaghian usingmachinelearningmethodsforlongtermtechnicalandeconomicevaluationofwindpowerplants AT huasong usingmachinelearningmethodsforlongtermtechnicalandeconomicevaluationofwindpowerplants |