Biography

Dr Mahmoud Dhimish is an Senior Lecturer specialising in solar photovoltaics, renewable energy systems, and artificial intelligence applications in engineering. His research focuses on integrating advanced modelling, machine learning, deep learning, and digital twin technologies—particularly for the solar

energy sector—to improve performance, enable real-time fault detection, and support intelligent recycling of end-of-life solar panels. He has published over 80 peer-reviewed papers, delivered 14 invited talks, and was recognised among the top five UK scientific academics by Stanford University in 2022. From 2021 to 2024, Mahmoud founded and led the MSc in Renewable Energy Technologies at the University of York. Previously, from 2019 to 2021, he served as Programme Lead for the BEng/MEng in Electronic and Electrical Engineering at the University of Huddersfield. He is now leading the creation of the MSc Intelligent Renewable Energy at the University College of the Built Environment.

Appointments

Mahmoud is currently a Visiting Research Associate Professor at the Technical University of Denmark. He serves as a committee member for the International Exchange Grants at the Royal Society (UK) and is a member of the EIC Pathfinder Challenges under the European Innovation Council. He also contributes to the academic community as a member of the Editorial Board for Clean Energy (Oxford University Press) and Renewable Energy (Elsevier).

Qualifications

and memberships

Teaching

Research Interests

Industry

Qualifications

and memberships

Qualifications

  • 2020 Diploma in Business Analytics: Decision Making Using Data, Cambridge Judge Business School, University of Cambridge, UK
  • 2019 Post Graduate Certificate in Higher Education (PGCert), School of Education, University of Huddersfield, UK
  • 2018 PhD (Ph.D. Scholarship, Renewable Energy Engineering), Title: Fault detection and performance analysis of photovoltaic installations, Department of Engineering, University of Huddersfield, UK
  • 2015 Master (Electronic and Communication Engineering), Department of Engineering, University of Huddersfield, UK
  • 2014 Bachelor (Telecommunications Engineering), March 2014, School of Engineering, PSUT University, Jordan

Memberships

  • FHEA (Fellow, Higher Education Academy)

Teaching

Mahmoud has previously taught a range of subjects at BSc and MSc levels, including:

  • Renewable Energy Engineering
  • Artificial Intelligence and Data Science
  • Solar Energy
  • Wind Energy
  • Power Electronics
  • Applied Machine Learning

Research Interests

My research centres on advancing renewable energy systems through the integration of artificial intelligence, with a particular emphasis on solar energy and wind farms. I develop and apply machine learning and deep learning techniques for performance forecasting, system optimisation, and real-time fault detection, enabling smarter, more resilient, and efficient energy infrastructure. By combining data-driven modelling with domain expertise, my work aims to maximise renewable energy output, extend asset lifespans, and reduce operational costs.

Industry

Mahmoud has collaborated extensively with industry over the past eight years, delivering innovative solutions at the intersection of renewable energy and advanced analytics. Most recently, he served as an industrial consultant for Above Surveying Ltd (UK), driving AI-enabled solar PV diagnostics. His previous collaborations include research partnerships with Trina Solar (China), National Grid (UK), PV Cycle (Belgium), and A-Safe (UK), spanning areas such as performance forecasting, fault detection, degradation analysis, and system optimisation.

Publications

Refereed Journal Articles (only selected from most recent published articles 2024-2025)
  • Bartholomäus, M., Riedel-Lyngskær, N., Morino, L., Dhimish, M., Stefanidis, P., Razanakoto, I., Deru, M., Poulsen, P.B. and Spataru, S.V., 2025. Field degradation of bifacial photovoltaic modules in northern Europe. Solar Energy, 300, p.113786.
  • Dhimish, M., d’Alessandro, V., Vieira, R., Mellit, A., Spataru, S.V. and Benatto, G.A.D.R., 2025. Evaluation of the Multi-Year-on-Year Method for Degradation Analysis in Photovoltaic Systems. Renewable Energy, p.123772.
  • Drir, N., Mellit, A., Bettayeb, M. and Dhimish, M., 2025. Enhanced Photovoltaic Defect Detection Using Perceptual Loss in DCGAN and VGG16-Integrated Models on Electroluminescence Images. IEEE Journal of Photovoltaics.
  • d’Alessandro, V., Daliento, S., Dhimish, M. and Guerriero, P., 2025, January. A Tool for a Fast and Accurate Evaluation of the Energy Production of Bifacial Photovoltaic Modules. In Solar (Vol. 5, No. 1, p. 2). MDPI.
  • Hassan, S. and Dhimish, M., 2024. Broad-scale Electroluminescence analysis of 5 million+ photovoltaic cells for defect detection and degradation assessment. Renewable Energy, 237, p.121868.
  • De Riso, M., Hassan, S., Guerriero, P., Dhimish, M. and Daliento, S., 2024. Enhanced Photovoltaic Panel Diagnostics: Advancing a High-Precision and Low-Cost IV Curve Tracer. IEEE Transactions on Instrumentation and Measurement.
  • Badran, G. and Dhimish, M., 2024. Comprehensive study on the efficiency of vertical bifacial photovoltaic systems: a UK case study. Scientific Reports, 14(1), p.18380.
  • Badran, G. and Dhimish, M., 2024. Short Term Performance and Degradation Trends in Bifacial Versus Monofacial PV Systems: A UK Case Study. IEEE Journal of Photovoltaics, 14(5), pp.861-864.
PhD Thesis
  • Dhimish, M., 2018. Fault detection and performance analysis of photovoltaic installations (Doctoral dissertation, University of Huddersfield).
Refereed Conference Papers
  • Hassan, S. and Dhimish, M., 2024, April. Exploring the Dynamics of Risk and Quality in Solar Photovoltaic Systems. In The Photovoltaics Science, Applications and Technology Conference-PVSAT-2024: PVSAT-2024.
  • Daliento, S., De Riso, M., Guerriero, P., Matacena, I., Dhimish, M. and d’Alessandro, V., 2024, June. On the optimal orientation of bifacial solar modules. In 2024 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) (pp. 396-400). IEEE.
  • Guerriero, P., Catalano, A.P., Scognamillo, C., Dhimish, M., Daliento, S. and D’Alessandro, V., 2023, June. Thermo-Electrochemical Simulations in SPICE of Li-Ion Batteries under Active Charge Balancing. In 2023 International Conference on Clean Electrical Power (ICCEP) (pp. 8-12). IEEE.
  • Dhimish, M., Holmes, V., Dales, M., Mather, P., Sibley, M., Chong, B. and Zhang, L., 2017, June. Fault detection algorithm for multiple GCPV array configurations. In 2017 IEEE Manchester PowerTech (pp. 1-6). IEEE.
  • Dhimish, M., Holmes, V., Dales, M., Mather, P., Sibley, M., Chong, B. and Zhang, L., 2017, June. The impact of cracks on the performance of photovoltaic modules. In 2017 IEEE Manchester PowerTech (pp. 1-6). IEEE.
  • Dhimish, M., Holmes, V. and Dales, M., 2016, September. Grid-connected PV virtual instrument system (GCPV-VIS) for detecting photovoltaic failure. In 2016 4th International Symposium on Environmental Friendly Energies and Applications (EFEA) (pp. 1-6). IEEE.

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