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Undoubtedly, the steady increase in the number of elderly people is not to be underestimated. These demographic changes call attention to new challenges regarding adequate aging-in-place strategies. Since the majority of the senior population spend up to 90% of their time indoors, appropriate and comfortable housing represents an important foundation for such strategies. In this respect, different types of data gathered from sensors, connected devices, and Internet of Things (IoT) technologies come to play an important role to support services for the elderly population in indoor environments. One of the aspects of concern is thermal comfort. In this paper, we introduce a new deep learning-based model framework named STCMS, that uses both Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN) architectures, dedicated to predicting thermal comfort for elderly people. Experiments based on the publicly available dataset, show that our proposed models outperform the pioneering approaches of the literature on the agreed assessment metrics, particularly with an accuracy that varies between 80.29% to 88.16% using only internal and external temperatures data. The proposed approach can be used as a smart thermostat solution integrated into smart home ecosystems dedicated to elderly people. Thus, it would contribute simultaneously to improve their well-being and to reduce energy consumption and health costs.

ISSN: 2641-8169 openalex: W3127167952