Concordia University

https://www.concordia.ca/content/concordia/en/sgs/postdoctoral-fellows/funding/horizon/descriptions/2003.html

Fellowship Description

Research program title

Energy Efficiency Enhancement in Buildings Based on a Data-Driven Approach

Reference number

2003

Supervisor

Program description

The rapidly growing and gigantic body of stored data in the building field, coupled with the need for data analysis, has generated an urgent need for powerful tools that can extract hidden but useful knowledge of building performance improvement from large data sets. As an emerging subfield of computer science, data mining (DM) technologies suit this need well and have been proposed for relevant knowledge discovery in the past several years.

To improve the energy efficiency in buildings, classical approaches that are based only on simulation and physical modelling face serious difficulties related to the accuracy of many input data such as weather, occupant behavior profile, availabilities of renewable energy sources, etc. This is why it is common to see that the measured energy consumption of a building system is different from what was expected based on the system design and sizing during which several assumptions are made (regarding the boundary conditions).

In this context and to address these shortcomings, the development of a new approach to enhance the energy efficiency in buildings, by acting on the integrated systems or by improving the occupant behavior, that is based on measured data is very promising.

Academic qualifications required

  • PhD and one year of PDF experience.
  • The candidate must have knowledge and expertise in the area of DM and machine learning as well as building simulation and energy efficiency.
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