Solar power plants are among the most widely used renewable energy sources today. Varying radiation levels from region to region, and similarly varying consumption depending on the user within a given region, make the optimal sizing of these plants challenging. In this study, a machine learning-based surrogate model for the real-time sizing optimization of solar power plants, trained with a completely original dataset, has been developed. In the first stage, 500 different solar power plant installation scenarios were synthetically generated and evaluated in HOMER, and the obtained optimal sizing outputs were used as training targets for the proposed surrogate model rather than real operational data. The results obtained by applying various machine learning methods to the generated dataset are presented comparatively. Among 7 different machine learning models, XGBoost, Gradient Boosting, and LightGBM demonstrated the best performance. The developed model achieved an average R² score of 0.9425 for a total of 3 targets, while target-specific performance showed R² scores of 0.9747 for inverters, 0.9365 for PV panels, and 0.9165 for batteries. This model serves as a computationally efficient surrogate of the HOMER optimization process, enabling high-accuracy real-time predictions while significantly reducing the computational burden associated with intensive mathematical calculations, iterative procedures, and complex search spaces.

Electrical energy is a fundamental requirement for the sustainability and technological development of modern life. The ability to use electrically powered systems in off-grid environments necessitates energy storage; to meet this need, battery and accumulator technologies have been developed. Increased performance and reduced costs in battery technologies have significantly expanded their application areas, particularly in the automotive sector. Battery Management Systems (BMS) play a critical role in ensuring the safe, efficient, and long-lasting operation of large-scale battery packs. Cell balancing systems, a fundamental component of BMS, improve system performance and battery life by correcting voltage and charge imbalances between series-connected cells. In this study, an active cell balancing model for a 13-cell battery pack was designed in the MATLAB/Simulink environment, and the system's performance was successfully analyzed. The obtained results were evaluated with the support of graphical data.