Scalable decarbonisation using automated operation optimisation

Abstract

One of the biggest challenges in facing the climate crisis is the decarbonization of the large and diverse building stock. A reduction of carbon dioxide emissions can be achieved by technical measures and engaging the building occupants to adapt their behaviour. Among the technical measures, implementing predictive control as an upgrade of the existing heating, ventilation, air conditioning and cooling system is especially promising as it allows reductions at potentially low running cost. However, the effort for adapting, implementing and deploying these methods to fit specific buildings and scenarios is high and requires special domain knowledge, hindering the wide-spread application. In this paper, we present a highly automated and data-driven implementation process utilizing an open-source container orchestration system, and the results from real-life case studies in existing buildings in which predictive control was retrofitted. Additionally, occupant information systems were installed in the buildings for increasing transparency about the building performance and the effect of the occupants’ behaviour. The shown method is useful for reducing the time required and manual effort for implementing new control strategies, and thus reducing carbon dioxide emissions while simultaneously increasing thermal comfort and air quality.