About the project
The need to reduce CO2 emissions makes energy in the built environment a key issue given that inefficient buildings represent around 35% of the total energy consumption. Continuous Monitoring (CM) and Fault Detection and Diagnosis (FDD) are complex but important technologies in detecting inefficiencies in Heating Ventilation Air Conditioning (HVAC). With a rise in demand for cooling caused by global warming, this project is developing a new approach based on data analytics and machine learning. This will enable resulting CM and FDD modules to be programmed in a state-of-the-art Building Management System (BMS) and become market-ready by the end of the project.
About the project
Installations in buildings are responsible for around 35% of all energy consumption, approximately 20% of which is due to inefficient operations. Inferior environmental conditions within classrooms can have both short- and long-term health effects, mainly due to the presence of particulate matter. With greater insights into sensors, data interpretation, trend signaling, continuous monitoring, fault detection/diagnosis and predictive maintenance, problems can be identified in the Heating Ventilation Air Conditioning (HVAC) systems of schools. The ECoS-IAQ project focuses on the creation of product development concepts for air handling manufacturers, air filter manufacturers, control companies and installers.
About the project
The goal is to develop a self-learning module that can monitor and diagnose climate systems in large buildings. This will enable a climate system to perform better; for instance, lower energy consumption, better thermal comfort and better air quality. More efficient maintenance is also possible. The module will be used as an add-on for the Building Energy Management System (BEMS) of offices.