A Stacking Ensemble Classifier-Based Machine Learning Model for Classifying Pollution Sources in Photovoltaic Panels

Machine Learning


  • Well, D.-B. Exploring the operational feasibility of forest photovoltaics using simulated solar trees. Science.member of parliament 121–12 (2022).

    Articles Google Scholar

  • LM Nut, W. Raza, YC Park A parametric study of a solar residential heating system with a seasonal underground thermal energy storage tank. sustainability 128686 (2020).

    Articles Google Scholar

  • Kannan, N. & Vakeesan, D. Solar Energy for the Future World: A Review. Update. keep. Energy Rev. 621092–1105 (2016).

    Articles Google Scholar

  • Sherwani, A. other. Life Cycle Assessment of Photovoltaic-Based Power Generation Systems: A Review. Update. keep. Energy Rev. 14540–544 (2010).

    Articles CAS Google Scholar

  • Swartz, B. other. Estimated loss of solar energy production due to air pollution in China since 1960 using surface radiation data. nut.energy Four657–663 (2019).

    Articles Google Scholar

  • Nadia, A.-R., Isa, NAM & Desa, MKM Advances in PV Tracking Systems: A Review. Update. keep. Energy Rev. 822548–2569 (2018).

    Articles Google Scholar

  • Magami, MR other. Power loss due to fouling of solar panels: A review. Update. keep. Energy Rev. 591307–1316 (2016).

    Articles Google Scholar

  • Cleaning, SP Why Clean Solar Panels? (2022). https://www.solarpanelcleaningltd.co.uk/why-clean-solar-panels/.

  • Tayel, SA, Abu El-Maaty, AE, Mostafa, EM & Elsaadawi, YF Self-cleaning and hydrophobic nano-coatings improve the performance of photovoltaic panels. Science.member of parliament 121–13 (2022).

    Articles Google Scholar

  • Sulaiman, South Australia other. Effect of dust on the performance of photovoltaic panels. world academy. Science. engineering technology. 58588–593 (2011).

    Google scalar

  • Zaihidee, FM, Mekhilef, S., Seyedmahmoudian, M. & Horan, B. Dust as an irreversible degradation factor affecting photovoltaic panel efficiency: why and how. Update. keep. Energy Rev. 651267–1278 (2016).

    Articles Google Scholar

  • Perera, KS, Aung, Z. & Woon, WL Machine Learning Technologies to Support Renewable Energy Generation and Integration: A Investigation.of International Workshop on Data Analysis for Renewable Energy Integration81–96 (Springer, 2014).

  • Khan, PW, Byun, Y.-C. & Lee, S.-J. Predicting Optimal Photovoltaic Panel Orientation and Tilt Angle Using Stacking Ensemble Learning. front.energy resistance Ten382 (2022).

    Articles Google Scholar

  • Waqas Khan, P. & Byun, Y.-C. Detection and Classification of Multiple Faults in Wind Turbines Using a Stacking Classifier. sensor twenty two6955 (2022).

    Articles ADS PubMed PubMed Central Google Scholar

  • Khan, PW other. A machine learning-based approach for predicting energy consumption of renewable and non-renewable power sources. energy 134870 (2020).

    Articles CAS Google Scholar

  • Bouzgou, H. & Gueymard, CA Minimal Redundancy and Maximum Relevance with Limit Learning Machines for Global Solar Radiation Prediction: Towards Optimized Dimensionality Reduction of Solar Time Series. Sol.energy 158595–609 (2017).

    Articles ADS Google Scholar

  • Yu, J., Wang, Z., Majumdar, A. & Rajagopal, R. DeepSolar: A Machine Learning Framework for Efficiently Building a Solar PV Installation Database in the United States. Jules 22605–2617 (2018).

    Articles Google Scholar

  • Mohajeri, N. other. City-scale roof shape classification using machine learning for solar energy applications. Update.energy 12181–93 (2018).

    Articles Google Scholar

  • Heinrich, M. other. Detection of cleaning interventions for photovoltaic modules with machine learning. applied energy 263114642 (2020).

    Articles Google Scholar

  • Martin, J., Jaskie, K., Tofis, Y., Spanias, A. Stain detection of PV arrays using machine learning.of 2021 IEEE International Conference on Information, Intelligence, Systems and Applications (IISA)1–6 (IEEE, 2021).

  • Liu, Y., Li, Y., Zhang, Y., Li, Z., Wang, X. Solar panel fouling classification using deep learning. research gate 111–9 (2021).

    Google scalar

  • Chang, W. other. Probabilistic estimation of photovoltaic fouling loss based on deep learning. IEEE transformer. keep.energy 122436–2444 (2021).

    Articles ADS Google Scholar

  • Yang, M., Ji, J. & Guo, B. Stain quantification using image-based methods: the effect of image conditions. IEEE J. Photobolt. Ten1780–1787 (2020).

    Articles Google Scholar

  • Mehta, S., Azad, AP, Chemmengath, SA, Raykar, V. & Kalyanaraman, S. Deepsolareye: Power Loss Prediction and Weakly Supervised Fouling Localization with a Fully Convolutional Network of Solar Panels.of 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)333–342 (IEEE, 2018).

  • Chulunsaikhan, T. other. Uses machine learning to predict solar panel power output based on weather and air pollution characteristics. Korea Multimed.society twenty four222–232 (2021).

    Google scalar

  • Zia, D. other. Evaluating machine learning models for predicting daily global and diffuse insolation under various weather/pollution conditions. Update.energy 187896–906 (2022).

    Articles Google Scholar

  • Kahn, PW, Byun, Y.-C. Adaptive error curve learning ensemble models for improving energy consumption prediction. Calculate. meter. Contin. 691893–1913 (2021).

    Google scalar

  • Sagi, O. & Rokach, L. Ensemble learning: a survey. Wiley Interdisciplinary. Know your minimum revision data. discob. 8e1249 (2018).

    Articles Google Scholar

  • Luther, L. other. Gradient boosting for high-dimensional prediction of rare events. Calculate. Statistics data anal. 11319–37 (2017).

    Articles MathSciNet MATH Google Scholar

  • Rodriguez-Galiano, VF, Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, JP Evaluating the effectiveness of random forest classifiers in land cover classification. ISPRS J. Photogram. remote. sense. 6793–104 (2012).

    Articles ADS Google Scholar

  • Sharaff, A. & Gupta, H. An extra-tree classifier using a metaheuristic approach for email classification.of Advances in computer communications and computational science189–197 (Springer, 2019).

  • Baak, M., Koopman, R., Snoek, H. & Klous, S. New correlation coefficients between categorical, ordinal, and interval variables with Pearson properties. Calculate. Statistics data anal. 152107043 (2020).

    Articles MathSciNet MATH Google Scholar

  • Piao, L. & Fu, Z. Quantification of unambiguous associations at different timescales: comparison of dcca and Pearson methods. Science.member of parliament 61–11 (2016).

    Articles Google Scholar

  • Visa, S., Ramsay, B., Ralescu, AL, Van Der Knaap, E. Confusion matrix-based feature selection. MAICS 710120–127 (2011).

    Google scalar

  • Goutte, C. & Gaussier, E. Probabilistic interpretation of precision, recall, and f-scores and their impact on assessment.of European Conference on Information Retrieval345–359 (Springer, 2005).

  • Baldi, P., Brunak, S., Chauvin, Y., Andersen, CA, Nielsen, H. Assessing the accuracy of prediction algorithms for classification: an overview. bioinformatics 16412–424 (2000).

    Papers CAS PubMed Google Scholar



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