Type: Master assignment
Contact (internal): Johann Hurink and Gerwin Hoogsteen
Company: Heijmans Energy Services
Predictiveness of energy savings in commercial building climate systems
Here’s an open door for you: commercial buildings use a lot of energy. The installations that ensure the building is a nice and comfortable environment (HVAC, lighting, etc.) are in many cases not optimally adjusted to the specific use of the building by its users. For this reason there is a large potential for improvement, where energy savings up to 20% can be realized. Currently this comes down to the specific attention and effort of the technical knowhow and experience of the energy advisor, who programs optimizations in the building management software.
This is not very scalable, and given urgency of the climate crisis we have to move faster and become smarter to get the job done. The challenge is how we can automate these adjustments – big and small – make them dynamically response and make them work seamlessly with the behavior of the building, its users and outside factors like weather. But also implementing data driven forecasting models and machine learning, to tap into a potential and insights that are unavailable to the naked eye. How can we use building and external data sources to predict behavior and energy consumption, in order to achieve an energy optimal buildings in the most cost effective manner?
The project is available in cooperation with Heijmans Energy Services. Contact Gerwin Hoogsteen and/or Johann Hurink for more information:
