Background and activities

Mohammadreza Aghaei received a Ph.D. degree in electrical engineering from Politecnico di Milano, Italy in 2016. He joined Fraunhofer ISE as visiting scholar in 2015. He was a Postdoctoral Scientist at Fraunhofer ISE and Helmholtz-Zentrum Berlin (HZB)-PVcomB, Germany, in 2017 and 2018, respectively. He has been joined to the Department of Sustainable Systems Engineering (INATECH), Solar Energy Engineering at the University of Freiburg, as a Guest Scientist and Lecturer in 2017. Later, he also fulfilled another two years postdoc in the Design of Sustainable Energy Systems Group at Eindhoven University of Technology (TU/e), in the Netherlands. He was also appointed as an Adjunct Professor at Amirkabir University of Technology (AUT) in 2020.  Since 2019, he has been the chair of the working group 2: reliability and durability of PV in EU COST Action PEARL PV, and currently, he supports WG2 as vice-chair.

Currently, he is working as a senior scientist (postdoctoral fellow) at the  Smart Buildings and Infrastructure group at the Norwegian University of Science and Technology (NTNU) in Norway. He is also the co-coordinator of an EU Horizon 2020 project “COLLECTiEF” - Collective Intelligence for Energy Flexibility. He is also an IEEE senior member. 

He authored numerous publications in internationally refereed journals, book chapters, and conference proceedings. His main research interests include Photovoltaics, Energy Transition, Autonomous Monitoring, Artificial Intelligence (AI), Unmanned Aerial Vehicle (UAV).


Scientific, academic and artistic work




  • Aghaei, Mohammadreza. (2020) Aerial infrared thermography for low-cost and fast fault detection in utility-scale PV power plants. Solar Energy.
  • Aghaei, Mohammadreza. (2020) Fault Detection and Classification for Photovoltaic Systems Based on Hierarchical Classification and Machine Learning Technique. IEEE transactions on industrial electronics (1982. Print).
  • Aghaei, Mohammadreza. (2020) Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics. Solar Energy.