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AI for Sustainable Engineering Lab
(AISEL)

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Welcome!

Our mission is to pioneer advancements at the intersection of artificial intelligence (AI) and sustainable engineering practices. Through innovative research and development, we aim to revolutionise renewable energy technologies, smart infrastructure, and sustainable systems. AISEL focuses on leveraging AI algorithms for predictive modeling, system optimisation, and data-driven decision-making. Our research extends to areas such as renewable energy integration, smart grid optimization, and sustainable materials.


Dr. Mahmoud Dhimish's leadership brings a wealth of expertise to the lab, with a vision to drive positive change in the field of sustainable engineering. With a dynamic team and state-of-the-art facilities, AISEL is committed to pushing the boundaries of what's possible in the pursuit of a greener, more sustainable future.
 

We welcome collaborations and partnerships that share our passion for sustainable engineering solutions. If you're interested in joining us on this transformative journey, please don't hesitate to get in touch.
 

Together, let's engineer a brighter, more sustainable tomorrow.

Featured Publications

Nature Scientific Reports, 2023

This paper introduces a highly accurate solar cell crack detection system using four CNN architectures. It assesses cell condition through electroluminescence images, achieving up to 99.5% acceptance rate. Thermal tests validate its effectiveness, highlighting potential PV industry benefits.

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Nature npj Materials Degradation, 2023

In this study, we analysed thermal defects in 3.3 million PV modules located in the UK. Our findings show that 36.5% of all PV modules had thermal defects, with 900,000 displaying single or multiple hotspots and ~250,000 exhibiting heated substrings. We also observed an average temperature increase of 21.7 °C in defective PV modules. Additionally, two PV assets with 19.25 and 8.59% thermal defects were examined for PV degradation, and results revealed a higher degradation rate when more defects are present. These results demonstrate the importance of implementing cost-effective inspection procedures and data analytics platforms to extend the lifetime and improve the performance of PV systems.

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International Journal of Hydrogen Energy, 2023

 

This paper presents an artificial neural network (ANN) for fault detection in PEM fuel cell systems, using existing input/output data. Experimental results show >90% accuracy in detecting pressure, consumption, and voltage faults without additional sensors. The model requires initial training but operates autonomously thereafter.

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Renewable Energy, 2023

This study assesses the reliability of 186 PV modules, tracking them from packaging to installation. Pre-packaging, no significant issues were found. After installation, 2.2% developed cracks, leading to output power losses. Thermal inspections revealed hotspots in cracked modules, indicating potential degradation. Additionally, PID tests showed greater impact on modules with cracks.

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In collaboration with:

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