Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE
This study addresses the fundamental challenge of accurately forecasting power generation from photovoltaic (PV) systems, which is crucial for effective grid integration and energy management. The intermittency and variability of solar power due to environmental factors pose significant difficulties...
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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/S259017452500090X |
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| author | Tareq Salameh Mena Maurice Farag Abdul-Kadir Hamid Mousa Hussein |
| author_facet | Tareq Salameh Mena Maurice Farag Abdul-Kadir Hamid Mousa Hussein |
| author_sort | Tareq Salameh |
| collection | DOAJ |
| description | This study addresses the fundamental challenge of accurately forecasting power generation from photovoltaic (PV) systems, which is crucial for effective grid integration and energy management. The intermittency and variability of solar power due to environmental factors pose significant difficulties in achieving reliable predictions. An adaptive neuro-fuzzy inference system (ANFIS) model is proposed for forecasting the performance of a 2.88 kW on-grid PV system in Sharjah, UAE. The model leverages extensive real-time data collected during the peak energy generation season to predict critical variables such as the maximum power point (MPP), voltage, and current. The ANFIS model achieves high prediction accuracy, with a Coefficient of Determination (R2) of 0.9967 for power generation, 0.9076 for voltage generation, and 0.9913 for current generation. These results highlight the model’s robustness in capturing the nonlinear dependencies between environmental factors and PV output. The study compares the ANFIS model with other established machine learning models, including Linear Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest. The ANFIS model outperforms these models in terms of prediction accuracy, demonstrating its superior generalization capabilities. The findings underscore the potential of the ANFIS model for robust forecasting and effective PV performance management, providing a reliable tool for early fault detection and system assessment. Future work will focus on integrating fault detection capabilities and extending model validation across different seasons to ensure a comprehensive investigation of the system dynamics under fluctuating weather conditions. |
| format | Article |
| id | doaj-art-011d0f6563134f888fd262546bb5ca9f |
| institution | DOAJ |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-011d0f6563134f888fd262546bb5ca9f2025-08-20T03:10:27ZengElsevierEnergy Conversion and Management: X2590-17452025-04-012610095810.1016/j.ecmx.2025.100958Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAETareq Salameh0Mena Maurice Farag1Abdul-Kadir Hamid2Mousa Hussein3Sustainable Energy and Power Systems Research Centre, Research Institute for Sciences and Engineering (RISE), University of Sharjah, Sharjah 27272, United Arab Emirates; Department of Sustainable and Renewable Energy Engineering, College of Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; Corresponding authors at: Department of Sustainable and Renewable Energy Engineering, College of Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates (T. Salameh) & Department of Electrical and Communication Engineering, United Arab Emirates University, Al Ain, United Arab Emirates (M. Hussein).Sustainable Energy and Power Systems Research Centre, Research Institute for Sciences and Engineering (RISE), University of Sharjah, Sharjah 27272, United Arab Emirates; Smart Automation and Communication Technologies Research Center (SACT), University of Sharjah 27272 Sharjah, United Arab EmiratesSustainable Energy and Power Systems Research Centre, Research Institute for Sciences and Engineering (RISE), University of Sharjah, Sharjah 27272, United Arab Emirates; Smart Automation and Communication Technologies Research Center (SACT), University of Sharjah 27272 Sharjah, United Arab Emirates; Department of Electrical Engineering, College of Engineering, University of Sharjah 27272 Sharjah, United Arab EmiratesDepartment of Electrical and Communication Engineering, United Arab Emirates University, Al Ain, United Arab Emirates; Corresponding authors at: Department of Sustainable and Renewable Energy Engineering, College of Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates (T. Salameh) & Department of Electrical and Communication Engineering, United Arab Emirates University, Al Ain, United Arab Emirates (M. Hussein).This study addresses the fundamental challenge of accurately forecasting power generation from photovoltaic (PV) systems, which is crucial for effective grid integration and energy management. The intermittency and variability of solar power due to environmental factors pose significant difficulties in achieving reliable predictions. An adaptive neuro-fuzzy inference system (ANFIS) model is proposed for forecasting the performance of a 2.88 kW on-grid PV system in Sharjah, UAE. The model leverages extensive real-time data collected during the peak energy generation season to predict critical variables such as the maximum power point (MPP), voltage, and current. The ANFIS model achieves high prediction accuracy, with a Coefficient of Determination (R2) of 0.9967 for power generation, 0.9076 for voltage generation, and 0.9913 for current generation. These results highlight the model’s robustness in capturing the nonlinear dependencies between environmental factors and PV output. The study compares the ANFIS model with other established machine learning models, including Linear Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest. The ANFIS model outperforms these models in terms of prediction accuracy, demonstrating its superior generalization capabilities. The findings underscore the potential of the ANFIS model for robust forecasting and effective PV performance management, providing a reliable tool for early fault detection and system assessment. Future work will focus on integrating fault detection capabilities and extending model validation across different seasons to ensure a comprehensive investigation of the system dynamics under fluctuating weather conditions.http://www.sciencedirect.com/science/article/pii/S259017452500090XAdaptive Neuro-Fuzzy Inference System (ANFIS)Power generation predictionSolar energyMachine learningFuzzy logicNonlinear prediction |
| spellingShingle | Tareq Salameh Mena Maurice Farag Abdul-Kadir Hamid Mousa Hussein Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE Energy Conversion and Management: X Adaptive Neuro-Fuzzy Inference System (ANFIS) Power generation prediction Solar energy Machine learning Fuzzy logic Nonlinear prediction |
| title | Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE |
| title_full | Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE |
| title_fullStr | Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE |
| title_full_unstemmed | Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE |
| title_short | Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE |
| title_sort | adaptive neuro fuzzy inference system for accurate power forecasting for on grid photovoltaic systems a case study in sharjah uae |
| topic | Adaptive Neuro-Fuzzy Inference System (ANFIS) Power generation prediction Solar energy Machine learning Fuzzy logic Nonlinear prediction |
| url | http://www.sciencedirect.com/science/article/pii/S259017452500090X |
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