15 Last checked 25 June 2020

Dr Mahmoud Dhimish is a Senior Lecturer in Electronics and Control Engineering at the University of Huddersfield, UK. He graduated with MSc. in Electronics and Communication Engineering (Distinction) from the University of Huddersfield. Following this, he gained a PhD entitled “Fault Detection and Performance Analysis of Photovoltaic Installations” also from the University of Huddersfield.

• Nov. 2019 – present: Senior Lecturer in Electronics and Control Engineering, School of Computing and Engineering, University of Huddersfield.

• May 2018 – Oct. 2019: Lecturer in Electronics and Control Engineering, School of Computing and Engineering, University of Huddersfield.

• April 2016 – April 2018: Lecturer in Computing and Engineering, Study Group, International Study Centre (ISC), University of Huddersfield.

• Dec. 2017 – April 2018: Postdoctoral Research Fellow, project title: “Digital Two-way Inhalation/Exhalation profile recorder for airway disease self-training and management to help patients achieve optimal inhalation manoeuvres”. School of Computing and Engineering, University of Huddersfield.

• Jan. 2016 – Dec. 2016: Research Collaborator, project title: “PV output power enhancement using new intelligent PV configurations”. School of Electronic and Electrical Engineering, Leeds University.

Areas of Research and Expertise:

[1].   Photovoltaic (PV) solar energy systems:

• Simulation: MATLAB, LabVIEW, SOLARGIS, PVsyst
• Installations: Rooftop, Façade
• Reliability analysis: Fault detection, Hot-spots, Micro-cracks
• Monitoring: Internet of Things (IoT), Big data analysis, High-performance computing (HPC), Statistics

[2].   Prediction, forecasting and data analysis techniques:

• Machine learning
• Artificial neural networks
• Neuro-fuzzy methods
• Self-organizing maps

[3].   Sensitivity, risk and uncertainty analysis:

• Monte Carlo simulation
• First-order reliability analysis
• Set-to-set mapping

[4].   Climatology:

• Global warming
• Remote sensing and data assimilation
• Climate modelling using artificial neural networks and probabilistic methods