Machine Learning-Based Predictive Maintenance for Photovoltaic Systems
The performance of photovoltaic systems is highly dependent on environmental conditions, with soiling due to dust accumulation often being referred to as a predominant energy degradation factor, especially in dry and semi-arid environments. This paper introduces an AI-based robotic cleaning system t...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2673-2688/6/7/133 |
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| author | Ali Al-Humairi Enmar Khalis Zuhair A. Al-Hemyari Peter Jung |
| author_facet | Ali Al-Humairi Enmar Khalis Zuhair A. Al-Hemyari Peter Jung |
| author_sort | Ali Al-Humairi |
| collection | DOAJ |
| description | The performance of photovoltaic systems is highly dependent on environmental conditions, with soiling due to dust accumulation often being referred to as a predominant energy degradation factor, especially in dry and semi-arid environments. This paper introduces an AI-based robotic cleaning system that can independently forecast and schedule cleaning sessions from real-time sensor and environmental data. Methods: The system integrates sources of data like embedded sensors, weather stations, and DustIQ data to create an integrated dataset for predictive modeling. Machine learning models were employed to forecast soiling loss based on significant atmospheric parameters such as relative humidity, air pressure, ambient temperature, and wind speed. Dimensionality reduction through the principal component analysis and correlation-based feature selection enhanced the model performance as well as the interpretability. A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. Results: Performance, based on accuracy, precision, recall, and F1-score, demonstrated that logistic regression and SVM provided optimal classification performance with accuracy levels over 92%, and F1-scores over 0.90, demonstrating outstanding balance between recall and precision. The KNN and decision tree models, while slightly poorer in terms of accuracy (around 85–88%), had computational efficiency benefits, making them suitable for utilization in resource-constrained applications. Conclusions: The proposed system employs a dry-cleaning mechanism that requires no water, making it highly suitable for arid regions. It reduces unnecessary cleaning operations by approximately 30%, leading to decreased mechanical wear and lower maintenance costs. Additionally, by minimizing delays in necessary cleaning, the system can improve annual energy yield by 3–5% under high-soiling conditions. Overall, the intelligent cleaning schedule minimizes manual intervention, enhances sustainability, reduces operating costs, and improves system performance in challenging environments. |
| format | Article |
| id | doaj-art-becfb0e715e74486911cece375e45034 |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-becfb0e715e74486911cece375e450342025-08-20T02:48:17ZengMDPI AGAI2673-26882025-06-016713310.3390/ai6070133Machine Learning-Based Predictive Maintenance for Photovoltaic SystemsAli Al-Humairi0Enmar Khalis1Zuhair A. Al-Hemyari2Peter Jung3Department of Communication Technology, Duisburg-Essen University, 47057 Duisburg, GermanyComputer Science Department, Faculty of Engineering and Computer Science, German University of Technology in Oman, Muscat 130, OmanDepartment of Mathematical and Physical Science, College of Science, University of Nizwa, Nizwa 616, OmanDepartment of Communication Technology, Duisburg-Essen University, 47057 Duisburg, GermanyThe performance of photovoltaic systems is highly dependent on environmental conditions, with soiling due to dust accumulation often being referred to as a predominant energy degradation factor, especially in dry and semi-arid environments. This paper introduces an AI-based robotic cleaning system that can independently forecast and schedule cleaning sessions from real-time sensor and environmental data. Methods: The system integrates sources of data like embedded sensors, weather stations, and DustIQ data to create an integrated dataset for predictive modeling. Machine learning models were employed to forecast soiling loss based on significant atmospheric parameters such as relative humidity, air pressure, ambient temperature, and wind speed. Dimensionality reduction through the principal component analysis and correlation-based feature selection enhanced the model performance as well as the interpretability. A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. Results: Performance, based on accuracy, precision, recall, and F1-score, demonstrated that logistic regression and SVM provided optimal classification performance with accuracy levels over 92%, and F1-scores over 0.90, demonstrating outstanding balance between recall and precision. The KNN and decision tree models, while slightly poorer in terms of accuracy (around 85–88%), had computational efficiency benefits, making them suitable for utilization in resource-constrained applications. Conclusions: The proposed system employs a dry-cleaning mechanism that requires no water, making it highly suitable for arid regions. It reduces unnecessary cleaning operations by approximately 30%, leading to decreased mechanical wear and lower maintenance costs. Additionally, by minimizing delays in necessary cleaning, the system can improve annual energy yield by 3–5% under high-soiling conditions. Overall, the intelligent cleaning schedule minimizes manual intervention, enhances sustainability, reduces operating costs, and improves system performance in challenging environments.https://www.mdpi.com/2673-2688/6/7/133PV systemsmachine learningpredictive maintenanceprincipal component analysissupport vector machinelogistic regression |
| spellingShingle | Ali Al-Humairi Enmar Khalis Zuhair A. Al-Hemyari Peter Jung Machine Learning-Based Predictive Maintenance for Photovoltaic Systems AI PV systems machine learning predictive maintenance principal component analysis support vector machine logistic regression |
| title | Machine Learning-Based Predictive Maintenance for Photovoltaic Systems |
| title_full | Machine Learning-Based Predictive Maintenance for Photovoltaic Systems |
| title_fullStr | Machine Learning-Based Predictive Maintenance for Photovoltaic Systems |
| title_full_unstemmed | Machine Learning-Based Predictive Maintenance for Photovoltaic Systems |
| title_short | Machine Learning-Based Predictive Maintenance for Photovoltaic Systems |
| title_sort | machine learning based predictive maintenance for photovoltaic systems |
| topic | PV systems machine learning predictive maintenance principal component analysis support vector machine logistic regression |
| url | https://www.mdpi.com/2673-2688/6/7/133 |
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