Empowering saving energy at home through serious games on thermostat interfaces

• Published in: Energy and buildings 2022-05, Vol.263, p.112026, Article 112026
• Main Authors: Méndez, Juana Isabel, Peffer, Therese, Ponce, Pedro, Meier, Alan, Molina, Arturo
• Format: Article
• Language: English
• Subjects: Adaptive thermal comfort; Adaptive thermostats interfaces; Air conditioning; ANN thermostats interfaces; Artificial neural networks; Computer & video games; Educational software; Energy conservation; Energy consumption; Energy costs; Energy simulation; HVAC; Interfaces; Neural networks; Serious games; Strategy; Thermal comfort; Thermostats; User type; Ventilation
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2022.112026

[Display omitted] •Thermostat Interactive Interfaces allow real-time decision-making.•Adaptive premises offer tailored HMI that address behavioral problems on thermostats.•Serious Games applied on Thermostats provide community decisions toward saving energy.•Tailored Adaptive strategies promote energy reductions without losing thermal comfort.•Personality traits, player type, and energy consumer give insights for personalized interfaces. The residential Heating Ventilation and Air-Conditioning (HVAC) system use around 3/5 of the total energy consumption. Connected thermostats optimize the HVAC operation; however, householders have personality traits that lead into behavioral and usability problems toward the thermostat’s interface usage. Thus, a serious game applied in the thermostat interface can balance entertainment and education. Therefore, thermostat interfaces must address strategies that reduce energy without losing thermal comfort. This paper proposed an interactive interface type and a predicted interface type based on an HVAC strategy and a Natural Ventilation strategy. These strategies measured the impact of adaptive thermal comfort, energy consumption, and costs. Hence, twelve energy models located in California (Concord, Riverside, Los Angeles, and San Diego) were simulated using EnergyPlus™ through LadybugTools. The first interactive interface included Serious Game elements, so the householder interacted with the date, location, and setpoint. The second interface predicted the energy consumption and thermal comfort during winter and summer in Concord by a two-layer feed-forward Artificial Neural Network structure. The proposed structure decreases the energy consumption by at least 62% without losing thermal comfort.