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Hooman Azad

Doctoral Researcher
Loughborough University
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I completed my undergraduate studies in applied physics. After starting my career in the HVAC industry and working as a sales support engineer and product manager with world-renowned Variable Refrigerant Volume (VRF) heat pump manufacturers, I became passionate about the field and earned my Master of Science in mechanical engineering. During my master’s studies, I was interested in modelling solar collectors. Later I tried to investigate the technical feasibility of integrating solar energy (both solar thermal collectors and PV panels) with advanced cooling/heating systems in locations with high levels of solar radiation to devise solar cooling (and heating) systems. I joined the ERBE CDT program in October 2021. I am a member of the Building Energy Research Group (BERG), which aims to transform the performance of buildings for a healthy, sustainable, and zero-carbon future. I am an expert in building energy modelling (BEM), with a special interest in low-energy buildings such as Passivhus buildings. After starting my PhD, I entered the world of probabilities, statistics and data science. I am using statistical modelling techniques in building energy modelling and investigating the applicability of the grey-box modelling techniques to predict the indoor air temperature in buildings with low space heating demand (low energy buildings) in the UK climate. My expertise and research interests include: Building energy modelling, statistical modelling in building energy, buildings physics, time series analysis, data driven modelling, HVAC systems and equipment. I am also an ASHRAE member and won second place in the 2022 ASHRAE Doctoral Researcher Student Competition, hosted by Loughborough University in May 2022.
The applicability of the RC modelling technique to predict the free-floating indoor air temperature of low-energy buildings during summertime

In this work, the operational data of a Passivhaus case study building will be collected and used to create RC models.  Operational data of buildings (particularly low energy buildings) typically cover smaller indoor temperature ranges than would be ideal for the identification of grey box parameters and thus, the building is not excited sufficiently during normal operation. Thus, in this PhD, the applicability of RC modelling under the circumstances of poor-information datasets will be investigated. Identifying the suitable RC model that can represent and describe this type of thermal dynamic, is the aim of this study. The model could be useful for other buildings characterized by superinsulation and a very low infiltration rate.