International Research Journal of Education and Technology

The development of a mathematical model for photovoltaic (PV) cells using machine learning (ML)-based optimization techniques represents a significant advancement in the field of renewable energy systems. This model, developed on Jupyter Notebook, leverages the power of computational tools and data-driven approaches to accurately predict the performance and efficiency of PV cells under varying environmental conditions. The primary objective of this study is to create a robust and scalable model that can optimize the design and operation of PV cells, thereby enhancing their energy conversion efficiency and reducing costs. The mathematical framework incorporates key parameters such as solar irradiance, temperature, and material properties, which are critical in determining the output characteristics of PV cells. Machine learning algorithms, including neural networks, support vector machines, and genetic algorithms, are employed to fine-tune these parameters and identify optimal configurations. The dataset used for training and validation includes historical weather data, experimental measurements from PV cells, and synthetic data generated through simulations. The model's accuracy is evaluated using metrics such as mean squared error (MSE) and root mean squared error (RMSE. The results demonstrate that the ML-based optimization techniques significantly improve the predictive accuracy of the model compared to traditional methods. Furthermore, the model's ability to adapt to different geographical locations and climatic conditions makes it a versatile tool for the global deployment of PV systems.

Key Words: Photovoltaic Cells, Renewable energy systems, Machine Learning, Mathematical Model, Neural networks, Genetic algorithms.