Machine Learning-Assisted Photovoltaic-Thermal (PVT) System for Enhanced Energy Efficiency
The key component of this project is the development of a hybrid solar energy system that simultaneously generates electricity and thermal energy, supported by machine learning to optimize performance in real time. The system integrates photovoltaic panels with thermal collectors to maximize solar resource utilization and overall energy output.
A smart control framework will be established, where machine learning algorithms regulate cooling flow rates and operating parameters to minimize panel overheating and enhance efficiency. The project also includes the design of an intelligent monitoring platform to track environmental conditions and system outputs, allowing predictive adjustments and fault detection.
In the broader context of sustainable energy use, the system will contribute to reducing dependence on conventional energy sources, while providing an adaptable and efficient solution suitable for residential, commercial, and municipal applications. A prototype will be developed and experimentally validated to demonstrate its effectiveness in improving energy efficiency and extending system lifespan.
Desired disciplines
- Engineering
- Computer Science
Team Leaders
Industry Partners
SIG Solar