Collective intelligence (CI) is a form of distributed intelligence emerging from collaborative problem solving and decision making. It has the advantages of simple communication and less need of data transfer and computationally extensive central decision making systems. This work implements CI in demand side management (DSM) of a hypothetical urban area in Stockholm, created based on the representative residential buildings in the city. A simple platform and algorithm are developed for modelling CI-DSM, considering the timescales of 15min for communication and applying or disapplying adaptation measures. According to the results, CI increases the autonomy of the system and decreases the heating demand of buildings effectively, consequently increasing the demand flexibility based on climate conditions. CI results in decreasing the energy demand considerably, decreasing the total heating demand over a year by around 50%.
Keywords collective intelligence, demand flexibility, climate flexibility, climate resilience, demand side management, urban energy system