Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring
<p dir="ltr">The unprecedented rise in global temperatures, extreme weather events, and the depletion of <u>natural resources</u> underscore the urgent need for <u>sustainable practices</u>. Energy-saving solutions are pivotal in mitigating these challenges by...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , |
| Published: |
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <p dir="ltr">The unprecedented rise in global temperatures, extreme weather events, and the depletion of <u>natural resources</u> underscore the urgent need for <u>sustainable practices</u>. Energy-saving solutions are pivotal in mitigating these challenges by reducing carbon emissions, lessening our reliance on finite energy sources, and ultimately contributing to a more resilient and environmentally conscious future. Thus, this paper presents a novel approach to enhancing energy efficiency within residential environments by integrating a <u>Digital Twin</u> (DT) on the Home-Assistant platform. Home-Assistant provides a user-centric approach to the <u>DT technology</u>, allowing homeowners to establish and oversee virtual replicas of their living spaces by incorporating a variety of<u> Internet of Things</u> (IoT) devices and sensors. Leveraging the capabilities of this open-source home automation platform, we have developed a sophisticated system for providing real-time <u>energy consumption data</u>, personalized energy-saving recommendations, and a data-driven occupancy detection mechanism. This system was rigorously tested in a controlled laboratory environment with multiple users, simulating diverse household scenarios. The DT technology enabled the creation of accurate virtual representations of users' physical environment, facilitating the optimization of energy-intensive devices and systems. Our data-driven occupancy detection approach utilized Machine Learning (ML) algorithms to intelligently determine room occupancy, allowing for precise energy management based on real-time usage patterns. The occupancy detection approach has proven highly effective, with a testing accuracy rate of 95.12% and f1-scores of 94.55%. Additionally, a mini-pilot study evaluated the <u>recommender system</u> and found an outstanding 80% favorable user reaction, demonstrating its efficiency in giving energy-saving advice. Moreover, the DT comprehensively represented the user's state of presence, their engagement with the connected appliances, and the ambient conditions of the lab. These results demonstrate significant potential for reducing energy waste and cost while maintaining user comfort. This research contributes to the growing field of <u>smart home</u> technology by showcasing the practical implementation of DT and data-driven strategies to promote sustainable and efficient energy practices in everyday living spaces.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy and Buildings<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.enbuild.2024.115151" target="_blank">https://dx.doi.org/10.1016/j.enbuild.2024.115151</a></p> |
|---|