Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning
Uncertainty in photovoltaic plants significantly affects performance and economic viability, making its management crucial for optimal design and operation. In this context, the present study offers a novel framework for comprehensively assessing the photovoltaic project development. Iran was select...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174525000443 |
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| author | Seyyed Shahabaddin Hosseini Dehshiri Bahar Firoozabadi |
| author_facet | Seyyed Shahabaddin Hosseini Dehshiri Bahar Firoozabadi |
| author_sort | Seyyed Shahabaddin Hosseini Dehshiri |
| collection | DOAJ |
| description | Uncertainty in photovoltaic plants significantly affects performance and economic viability, making its management crucial for optimal design and operation. In this context, the present study offers a novel framework for comprehensively assessing the photovoltaic project development. Iran was selected as a case study, and using the k-means algorithm, the area was divided into eight clusters. Subsequently, a ten Megawatts photovoltaic plant was assumed in each region, and a comprehensive feasibility assessment was conducted, encompassing energy, economic, environmental, exergy, exergoeconomic, enviroeconomic, and energoeconomic aspects. Finally, the uncertainties were simulated using the Monte Carlo method. The gradient boosting regressor machine learning algorithm was predicted the PV plant for 10,000 different scenarios (R2 ∼ 98 %). The results showed the capacity factor and exergy efficiency of photovoltaic panels across Iran ranged from 23-27 % and 19.3–20.6 %, respectively. Additionally, the Levelized cost of electricity and payback period were in the range of 48–56 $/MWh and 13–16 years, respectively. Also, Monte Carlo analysis showed the probability of project failure for developing photovoltaic power plants in Iran was between 14–26 %. This assessment demonstrates the PV project development in Iranian cities is feasible and can serve as a reference for fossil-fuel-rich countries. |
| format | Article |
| id | doaj-art-18f08293c3b0455f8d2d5ef5600504e6 |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-18f08293c3b0455f8d2d5ef5600504e62025-02-09T05:01:09ZengElsevierEnergy Conversion and Management: X2590-17452025-04-0126100912Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learningSeyyed Shahabaddin Hosseini Dehshiri0Bahar Firoozabadi1Corresponding author.; Department of Mechanical Engineering, Sharif University of Technology, Tehran, IranDepartment of Mechanical Engineering, Sharif University of Technology, Tehran, IranUncertainty in photovoltaic plants significantly affects performance and economic viability, making its management crucial for optimal design and operation. In this context, the present study offers a novel framework for comprehensively assessing the photovoltaic project development. Iran was selected as a case study, and using the k-means algorithm, the area was divided into eight clusters. Subsequently, a ten Megawatts photovoltaic plant was assumed in each region, and a comprehensive feasibility assessment was conducted, encompassing energy, economic, environmental, exergy, exergoeconomic, enviroeconomic, and energoeconomic aspects. Finally, the uncertainties were simulated using the Monte Carlo method. The gradient boosting regressor machine learning algorithm was predicted the PV plant for 10,000 different scenarios (R2 ∼ 98 %). The results showed the capacity factor and exergy efficiency of photovoltaic panels across Iran ranged from 23-27 % and 19.3–20.6 %, respectively. Additionally, the Levelized cost of electricity and payback period were in the range of 48–56 $/MWh and 13–16 years, respectively. Also, Monte Carlo analysis showed the probability of project failure for developing photovoltaic power plants in Iran was between 14–26 %. This assessment demonstrates the PV project development in Iranian cities is feasible and can serve as a reference for fossil-fuel-rich countries.http://www.sciencedirect.com/science/article/pii/S2590174525000443Solar energyExergoeconomicMachine LearningMonte Carlo Simulation7E AnalysisPhotovoltaic |
| spellingShingle | Seyyed Shahabaddin Hosseini Dehshiri Bahar Firoozabadi Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning Energy Conversion and Management: X Solar energy Exergoeconomic Machine Learning Monte Carlo Simulation 7E Analysis Photovoltaic |
| title | Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning |
| title_full | Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning |
| title_fullStr | Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning |
| title_full_unstemmed | Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning |
| title_short | Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning |
| title_sort | robust analysis of photovoltaic plants a framework based on prediction uncertainties by machine learning |
| topic | Solar energy Exergoeconomic Machine Learning Monte Carlo Simulation 7E Analysis Photovoltaic |
| url | http://www.sciencedirect.com/science/article/pii/S2590174525000443 |
| work_keys_str_mv | AT seyyedshahabaddinhosseinidehshiri robustanalysisofphotovoltaicplantsaframeworkbasedonpredictionuncertaintiesbymachinelearning AT baharfiroozabadi robustanalysisofphotovoltaicplantsaframeworkbasedonpredictionuncertaintiesbymachinelearning |