Developing Energy Communities with Intelligent and Sustainable Technologies – First Results

In a recently started scientific project aiming at “Developing Energy Communities with Intelligent and Sustainable Technologies” (DE-CIST), we combine physical data on buildings in Rotterdam (The Netherlands) with socio-economic data from neighbourhoods and input from citizens and communities. Individual building data, together with meteorological, air quality, and GHG emission data, are processed by a novel AI solution classifying neighbourhoods and buildings based on their current status of energy sustainability, and their energy saving and emission reduction potential. This, in turn, informs measures that fit best per building and per neighborhood. Yet, to reveal which buildings or neighbourhoods are the worst off, we approach the problem using a socio-technological transitions perspective, which takes into account the needs and concerns of all citizens, notably the ones of the most vulnerable populations to reveal energy poverty and injustice. Using this approach, we will show which neighbourhoods can benefit the most, technically as well as socially.

Our presentation will start with an overview of the DE-CIST project and demonstrate how the combination of environmental and social information can make the energy transition process more efficient, economically viable, equitable, and more human. From recent analysis we conclude that energy communities have a strong effect on trust and engagement, fostering environmental awareness and motivation to save energy. In our presentation we will provide further insights into energy efficiency and renovations of buildings, and into how we can realize a fair, coherent energy transition process using a combination of results from AI-based methods, environmental modelling (of air pollution), and our analysis of the interviews with stakeholders and survey data.