Using collective intelligence to enhance demand flexibility and climate resilience in urban areas

Collective intelligence (CI) is a form of distributed that emerges in collaborative problem solving and decision making. This work investigates the potentials CI demand side management (DSM) urban areas. used to control energy performance representative groups buildings Stockholm, aiming increase flexibility climate resilience scale. CI-DSM developed based on simple communication strategy among buildings, using forward (1) backward (0) signals, corresponding applying disapplying adaptation measure, which extending indoor temperature range. A platform algorithm are for modelling CI-DSM, considering two timescales 15 min 60 min. Three scenarios represent typical, extreme cold warm years Stockholm. Several indicators assess including Demand Flexibility Factor (DFF) Agility (AF), defined explicitly this work. According results, increases autonomy agility system responding shocks without need computationally extensive central making systems. helps gradually effectively decrease absorb shock during events. Having finer timescale side, resulting faster variations, shorter engagement return normal conditions consequently higher resilience.