Data-driven Fault Detection Diagnostics for multi-residential building

at  Cenaero

Data-driven Fault Detection Diagnostics for multi-residential building
Cenaero (http://www.cenaero.be) is an applied research center that provides numerical simulation methods and tools to companies involved in a technology innovation process, allowing to invent and design more competitive products. Our ambition is to be internationally recognized as a technology leader in modeling and numerical simulation, to be a strategic partner of large global industries as well as a real support to regional companies including innovative SMEs. We are mainly active in the aerospace, process engineering, energy and building sectors, and provide expertise and engineering services in multidisciplinary simulation, design and optimization in the fields of mechanics (fluid, structure, thermal and acoustics), manufacturing of metallic and composite structures. Having a solid and recognized experience in numerical simulations and the development of methodologies and tools for turbomachinery applications, Cenaero is an official strategic R&D partner of the Safran group.
In addition to providing engineering services and software development, Cenaero operates a Tier-1 supercomputing infrastructure (tier1.cenaero.be). Our headquarters are located in Gosselies (Belgium), with a subsidiary office near Paris (France).
To support the expanding research activities focusing on energy management of building systems, we are looking for an intern student.

INTERNSHIP DESCRIPTION

The proposed research work is part of a building energy management project at a multi-residential building scale. The goal of the project is to provide the building owner with a tool for water and energy data analysis and FDD (Fault Detection and Diagnosis) functionalities based on the usage of a numerical building energy model and monitoring data (from IoT device) processing. We are looking for an intern student to work on time-series data processing including clustering and forecasting to contribute to the data-driven FDD methodologies. The objective is to use Machine Learning techniques on existing datasets to detect anomalies in the energy systems operation as well as in the building behavior (i.e. water and energy consumption and thermal comfort).
The length of the internship (3 to 6 months) may vary depending on the availability of the candidate.

Please refer the Job description for details