Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability
Accurate prediction of power output from a photovoltaic (PV) system is crucial for ensuring operational efficiency. This study addresses the challenge of predicting plant-scale PV power output by integrating automated machine learning (Auto-ML) with explainable modeling techniques. The integrated ap...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020814 |
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| Summary: | Accurate prediction of power output from a photovoltaic (PV) system is crucial for ensuring operational efficiency. This study addresses the challenge of predicting plant-scale PV power output by integrating automated machine learning (Auto-ML) with explainable modeling techniques. The integrated approach enhances predictive accuracy, supporting well-informed decision-making in power systems through data-driven frameworks. Real PV power data from a plant at Universiti Tun Hussein Onn Malaysia (UTHM) and five key weather parameters were used in this experiment. Auto-ML was employed to automatically identify the best-performing models tailored to the dataset. The top four performing models, achieving the highest predictive accuracies, were identified as Extra Tree (91% accuracy), Random Forest (85%), XGBoost (75%), and Decision Tree (68%) for further analysis. Their performance was then validated against commonly used artificial neural networks (ANN) and support vector machines (SVM) using multiple evaluation metrics including prediction accuracy, error rates, and interpretability. The results clearly demonstrate the superiority of the proposed approach across all performance metrics. For practical applications, a novel data mining method is also proposed to identify primary environmental drivers of PV performance using bivariate data analysis. Additionally, the model-based role of each parameter in the machine learning (ML) context is assessed using additivity of feature importance to uncover the underlying predictive mechanism of each ML model. This study establishes an advanced and powerful framework combining Auto-ML and explainable AI for predictive modeling of PV power output. It sets new standards for significantly improved operational decisions and a broader integration of AI in renewable energy forecasting for data-driven optimization in power systems. |
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| ISSN: | 2590-1230 |