User Adapting System Design for Improved Energy Efficiency During the Use Phase of Products: Case Study of an Occupancy-Driven, Self-Learning Thermostat

Winner of the EcoDesign2015 Best Paper Award.Tailoring products to user requirements can improve the energy efficiency without sacrificing user satisfaction. This study considers the case of an occupancy-driven smart thermostat in an office environment. Identifying patterns in past user behaviour enables occupancy prediction to control the heating accordingly. Potential energy savings and related environmental impact reductions, compared to a fixed schedule heating system, are calculated for various heating and building types in three regions (Leuven, Calgary and Tokyo) to account for variations in climate. The obtained energy savings range between 93.2 and 546.5 kWh per year and environmental impact reductions between 2 and 38 EcoPoints.status: publishe