Machine Learning Based Investigation of Behavioral Modeling of the MPPT based Solar PV System Behaviors Evaluation

Abstract

Improving the effectiveness of a solar PV system using an MPPT-based tracker has recently been an active study area. This study presents a behavioral analysis of photovoltaic (PV) cell performance under varying temperature conditions. The investigation is using a combination of theoretical modeling and machine learning techniques. A MATLAB simulation is developed to investigate the current-voltage (I-V) characteristics of a PV cell, considering the impacts of temperature on key parameters like short-circuit current (Isc) and open-circuit voltage (Voc). A ML based solution is designed using regression treeapproach employed to predict the MPPT based on temperature. in turn paper demonstrating the potential of data-driven techniques in PV performance prediction. The results highlight the importance of considering temperature effects in PV system design and optimization. The proposed methodology, combining theoretical modeling and machine learning, offers a powerful tool for analyzing and predicting PV cell performance across a wide range of operating temperatures, paving the way for enhanced efficiency and reliability in solar energy systems.

Introduction

This research focuses on enhancing the efficiency of solar photovoltaic (PV) systems by integrating Maximum Power Point Tracking (MPPT) and machine learning (ML). MPPT is a key technique that ensures solar modules operate at their optimal power output, which varies based on irradiance, temperature, and environmental conditions.

?? Core Objectives:

• Develop a behavioral model of a PV system.

• Analyze how temperature variations affect the maximum power point (MPP).

• Use machine learning (e.g., regression trees) to predict MPP under dynamic conditions.

• Simulate I-V and P-V curves using mathematical models and validate with experimental or ML-generated data.

???? Key Modeling Components:

• I-V Equation: Derived from the single-diode model.

• Short-Circuit Current (Isc): Modeled based on irradiance and temperature.

• Open-Circuit Voltage (Voc): Modeled as inversely proportional to temperature.

• Thermal Voltage (Vt): Based on Boltzmann’s constant and electron charge.

• Boost Converter: Used to regulate voltage with duty cycle control.

???? Machine Learning Integration:

• ML models, especially regression trees, are used to predict MPP based on temperature.

• ML helps capture nonlinear relationships and allows for real-time forecasting.

• While effective, tree-based models can introduce discrete jumps in predictions.

???? Experiments & Findings:

1. Impact of Solar Irradiance:

   • Higher irradiance increases current and overall power.

   • Voc changes slowly with irradiance.

2. Impact of Temperature:

   • Voc decreases with rising temperature, reducing MPP.

   • Current stays nearly constant or slightly increases.

3. ML-Based Prediction of MPP:

   • Regression trees trained on temperature vs. MPP data show high accuracy (R² = 0.9999, MSE = 0.0003).

   • Predicted results closely match simulation but have discrete transitions due to model structure.

???? Literature Review Insights:

• Numerous studies emphasize:

  • Mathematical PV modeling (Nguyen, Prakash).

  • MPPT controller design (Shanthi).

  • Parameter extraction (Lekouaghet).

  • ML applications in forecasting and diagnostics (Porowski, Mahesh, Lari).

  • Experimental validation (Ardeleanu).

• Growing trend in combining simulation with AI techniques for improved PV system performance.

? Expected Outcomes:

• Development of a robust, temperature-aware MPPT model.

• Improved power prediction and system adaptability via ML.

• Enhanced potential for real-time control and maintenance planning in solar PV systems.

Conclusion

The study provides a comprehensive evaluation of solar PV module behavior under the impact of varying thermal temperature and solar irradiance conditions. The proposed method integrating both theoretical modeling and ML based prediction techniques. Results confirm that temperature have significant influence on PV performance, primarily through reductions in open-circuit voltage and overall efficiency. Higher temperatures decrease Voc and the current Iscremains stable or slightly increasesEfficiency and power output reduce at higher temperatures. While irradiance directly enhances current generation and power output. The implementation of a regression tree algorithm for maximum power point (MPP) prediction demonstrated exceptional accuracy, with minimal error and strong correlation to simulated data. Regression tree model demonstrates high accuracy with Mean Squared Error (MSE) of 0.0003 and significant R² value of 0.9999. This justifies the selection of tree model. This validates the potential of machine learning as a predictive tool in solar energy systems. Moreover, the fusion of simplified linear models with data-driven learning offers a scalable and interpretable framework for dynamic performance analysis. These findings emphasize the critical role of temperature-aware modeling and intelligent control strategies in optimizing PV system design, and pave the way for further refinement of predictive algorithms to enhance granularity and real-world applicability. In future it is recommend to investigate advanced cooling techniques to mitigate temperature-induced efficiency losses in PV cells. Develop more sophisticated machine learning models in future may incorporating multiple environmental factors beyond temperature, such as humidity and dust accumulation.

References

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Copyright

Copyright © 2025 Devendra Chourasia, Dr. Rishi Chobhey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.