Buildings and districts

Building & energy performance optimisation

In the transition to a carbon-free future, a crucial role is reserved for buildings, local communities and cities. Planning this energy transition requires insights in the current building stock and local context, to find an optimal balance between energy efficiency measures, renewable energy generation technologies and smart and flexible control.

EnergyVille has unique datasets, algorithms, living labs and decision support tools to assist public and private actors in the energy transition of the building stock. Our technological solutions are being applied by a broad range of actors in the value chain, including major financial institutes, social housing companies, local governments and over 55 urban test areas across Europe.

Building materials, buildings and energy technology life cycle evaluation 

Buildings and their technologies play a crucial role in the transition towards a sustainable built environment. The built environment (including homes, buildings, energy services, streets, sidewalks, open spaces, transportation options, and more) has a significant influence on climate change, energy consumption and depletion of natural resources. EnergyVille conducts research on next generation sustainability assessment methodologies to make buildings, building materials, technical equipment and renewable energy systems more environmentally friendly, circular, and economically feasible. We do this for example by applying Life Cycle Assessment (LCA), Life Cycle Costing (LCC) and circularity assessment at different scale levels. EnergyVille uses the results to develop new technological insights and tools, to help companies during their product development and marketing strategies and to support decision making on regional, national and European level.

Home energy management systems

EnergyVille has a lot of experience with the control and optimisation of thermal and electrical energy installations in residential and commercial buildings. From state-of-art model predictive control techniques to advanced data-driven machine learning, there is always an optimal controller to combine the user comfort requirements with low costs, minimal emissions or for maximal integration of renewables. Algorithms and their implementation in real systems are key.