School of Engineering

Staff Profile

Dr Mehdi Pazhoohesh

Research Associate


Personal profile

Mehdi received his PhD in Smart Energy from University of Liverpool in 2017 and  joined Newcastle University in 2018. He has a background in the field of building energy and human comfort science and currently works as research associate within the National Canter for Energy System Integration (CESI) at Newcastle University. Most recent duties involved deploying machine learning and Artificial Intelligence techniques to deal with partial data sets and missing data imputation under big data analytics of building energy. Mehdi’s research interests fall mainly into Digital Energy, data analysis, Artificial Intelligence and particularly the applications of machine learning in building and energy sectors.Additionally, he is endorsed by the UK Research and Innovation (UKRI)  as a global talent in Energy Engineering in 2020.

Research group affiliations

1) National Centre for Energy Systems Integration

2) Active Building Centre

Research interests/expertise

Digital Energy, Energy in Buildings, Monitoring buildings, Modelling, Thermal Comfort in built environment, Machine learning, Data mining


BSc Electrical Engineering and power systems, MSc Energy and sustainability with electrical power systems, PhD Smart Energy for buildings

Honours and awards

Endorsed under Global Talent by the UK Research and Innovation, 2020

RISGC (Research Institute for Smart and Green Cities) funded proposal

Dushu Lake Higher Education Town (HET) grant,

Best PhD Poster, 1st Doctoral Students’ Poster Day

Full Scholarship for 4 years for PhD career 2012

Professional licences and certificates

TEFL certification

Energy audit Certificate

Solar Energy Certificate

Consultancy work

Energy Data Taskforce

The Energy Data Taskforce, commissioned by Government, Ofgem, and Innovate UK, has set out five key recommendations that will modernize the UK energy system and drive it towards a net zero carbon future through an integrated data and digital strategy throughout the sector. The first report was published on 13th June 2019 . 

Professional esteem indicators


Journal Referee:  Renewable Energy, Building and Environment, International Journal of Computing and Digital Systems, Journal of Architectural Engineering

Conference Referee: Technical Program Committee IEEE 12th International Conference CICN 2020, IEEE 7th International Conference EECSI 2020 

Research Impact


  • Introduce a novel methodology for construction progress monitoring in concrete structures using infrared thermography
  • Develop an occupancy-driven HVAC system to save energy inside commercial buildings by at least %11
  • Develop new missing data patterns for building and energy databases
  • Develop a novel imputation algorithm (KNN in row) for building and energy studies
  • A systematic approach to select the most suitable technique for treating missing values in datasets



Active Building Centre

(£36m project)


The £36m EPSRC Active Building Centre project will investigate the potential for large scale role out of domestic active buildings for the new build sector. Researchers at Newcastle University shall investigate the impact of active buildings on the energy networks, smart control of building elements, and the role of electric vehicles. Overall, energy demand, generation, storage, and transport combine to enable the active building to be controlled to offer services to local networks, as well as ensuring energy services to the building occupant. As the leader of data curing and imputation, I am responsible of providing guidance, instruction, and leadership to ABC team on data curing and data Imputation tasks.

Building as a Power Plant

This EPSRC project will investigate the feasibility of the Urban Sciences Building (Newcastle University) to offer services to the local network. The building is an interesting case study since it includes electricity generation, storage and demand, and thermal generation, storage and demand.

Co-Investigator:  Occupancy-driven intelligent control of HVAC system for saving energy and enhancing thermal comfort 

Research Institute for Smart and Green Cities (RISGC)   

This project aims to propose an intelligent control of the HVAC system to meet the requirements in terms of energy saving and thermal comfort at the same time. Algorithms will be proposed to analyse individual’s thermal preferences, behaviour patterns, and real-time locations and to predict the presence situation in the target room. Thermal preference of individuals will be investigated using computational fluid dynamics simulation and a survey corresponding to different scenarios. Historical behaviour data of the occupants will be collected, including daily schedule, arriving and leaving time, short-term and long-term leaves. Those data will be used to train the proposed algorithm and to predict the occupants’ absent/present status in the room in real time, which will decide the mode change of HVAC outlets based on the presented occupants' thermal preferences.

Principle-Investigator: Dushu Lake Higher Education Town (HET) Grant

Indoor localization approach for energy efficiency in buildings