Abstract
Residential energy efficiency decreases electricity consumption and saves energy worldwide. Moreover, 86% of the residential buildings use thermostats that control the Heating, Ventilation, and Air-Conditioning (HVAC) system. The energy consumption can decrease from 11% to 18% due to user behavior modifications accompanied by strategies such as feedback and gamification. Social products use gamification features to interact with the end-user and other devices. However, tailored gamified Human-Machine Interfaces (HMI) must consider five personality traits. As a result, implementing Artificial Neural Networks (ANN) within the HMIs can classify the type of user and deploy a tailored strategy to save energy and promote behavioral changes. Thus, this paper shows a three-step framework to develop a rapid prototype using Arduino and MATLAB/Simulink to predict which type of gamified interface is needed depending on the personality traits.
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Acknowledgments
Research Project supported by Tecnologico de Monterrey and CITRIS under the collaboration ITESM-CITRIS Smart thermostat, deep learning, and gamification project (https://citris-uc.org/2019-itesm-seed-funding/).
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Méndez, J.I. et al. (2021). A Rapid HMI Prototyping Based on Personality Traits and AI for Social Connected Thermostats. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_18
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DOI: https://doi.org/10.1007/978-3-030-89820-5_18
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