“Research is what I’m doing when I don’t know what I’m doing.
– Wernher von Braun
Research in the Intelligent Environments Laboratory focuses on approaches that allow for resilience and adaptation in the operational phase of buildings. We integrate
- Data analytics, machine learning and artificial intelligence
- Occupant comfort and behavior
- Architectural design and control systems
- Renewable energy systems and environmental monitoring
and innovate at the intersection of these areas.
We are reinventing the built environment such that it adapts to the occupants and varying internal and external influences.
About 40% of global energy demand and a similar fraction of anthropogenic greenhouse gas (GHG) emissions can be contributed to the built environment. At the same time, buildings have a 50-90% emission reduction potential using existing technologies and their widespread implementation, and should, therefore, be at the center of our focus to address climate change.
Since operational energy dominates the life-cycle energy use of a building, with about 70% share, research at IEL focuses on the operational energy of buildings, and aims at developing control systems that optimize energy use in operation across the building stock. Challenges that need to be overcome are
Challenge 1: Intelligent energy management solutions need to address the fact that the building stock is extremely heterogeneous, and, thus, solutions are needed that can adapt to both this heterogeneity as well as changes throughout the lifespan of buildings. For example, distributed energy generation, such as photovoltaics (PV), combined with electrical storage offers the potential to reduce the load on the electrical grid, and contribute to removing polluting power plants. However, such integration has to be carefully managed to avoid black outs and maintain service in all areas.
Challenge 2: Because up to 30% of the energy use in a building can be directly attributed to occupants, we need to incorporate their behavior and preferences directly and non-intrusively into the operation of buildings. Humans however, are notoriously unpredictable, so modeling and prediction of individuals is challenging. Interestingly, focusing on occupants and providing healthy and comfortable environments in homes and offices, could be also the path to energy efficient buildings.
Both challenges have in common that they require adaptation to individual entities, i.e., a self-tuning feature. In addition, both need simple-to-implement systems that can be scaled across large populations. Therefore, IEL researches adaptive and scalable algorithms, that can be applied to both. One such method is Reinforcement Learning, which is particularly suitable to develop Intelligent Environments.