Vol. 4 No. 1 (2024): Issue 4
Articles

Artificial Intelligence-Driven Optimization of Bifacial Solar Panel Performance in Complex Urban Environments

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  • Minji Lee ,
  • Jihoon Kim,
  • Hyejin Park
Minji Lee
EcoDynamics Tech
Jihoon Kim
FuturePower Systems
Hyejin Park
FuturePower Systems

Published 2024-12-29

Keywords

  • Artificial Intelligence,
  • Bifacial Solar Panels,
  • Urban Settings Efficiency Enhancement,
  • Machine Learning,
  • Geometric Modeling

How to Cite

Lee , M., Kim, J., & Park, H. (2024). Artificial Intelligence-Driven Optimization of Bifacial Solar Panel Performance in Complex Urban Environments. Energy & System, 4(1), 86–97. Retrieved from https://ojs.mri-pub.com/index.php/ES/article/view/24

Abstract

This study explores the optimization of bifacial solar panel performance in intricate urban landscapes through the application of artificial intelligence methodologies. Utilizing data from esteemed repositories such as the National Renewable Energy Laboratory (NREL) for irradiance, Weather Underground for meteorological conditions, and OpenStreetMap for urban topography, alongside performance metrics from solar panel manufacturers. The research methodology encompassed data preprocessing, the development of urban geometric models, and the implementation of a neural network for performance forecasting. The neural network demonstrated a notable accuracy of 0.92 and an F1-score of 0.90. Subsequent optimization via genetic algorithms pinpointed ideal orientations and inclinations, substantially augmenting the anticipated energy yield. Empirical validation through rigorous testing yielded a minimal Root Mean Square Error (RMSE) of 0.22 kW, affirming the precision of the proposed framework. This integrative AI-driven strategy offers a potent solution for enhancing the efficacy of bifacial photovoltaic installations in metropolitan contexts

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