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Decision support for building thermal comfort monitoring with a sustainable GenAI system

Arslan, Muhammad; Munawar, Saba; Mahdjoubi, Lamine; Manu, Patrick

Authors

Muhammad Arslan

Saba Munawar

Profile image of Lamine Mahdjoubi

Lamine Mahdjoubi Lamine.Mahdjoubi@uwe.ac.uk
Professor in Info. & Communication & Tech.

Patrick Manu Patrick.Manu@uwe.ac.uk
Professor of Innovative Construction and Project Management



Abstract

Staff expenses can account for up to 70% of business costs, with indoor conditions playing a critical role in employee health, behavior, and productivity. Optimal thermal comfort, typically around 21°C with 40-70% humidity, maximizes productivity. However, effective monitoring requires comprehensive data, particularly as energy regulations push for smarter building management. Although Building Information Modeling (BIM, a digital representation of a building's physical and functional characteristics) and sensor integration support facilities management, many existing systems are proprietary, expensive, and inflexible. To address these challenges, this study introduces ThermalComfortBot, a sustainable Generative Artificial Intelligence (GenAI)-powered Chatbot designed as an advanced Information System (IS). Utilizing Large Language Models (LLMs, AI models for natural language understanding) and Retrieval-Augmented Generation (RAG, a method that combines data retrieval with LLMs-generated insights), ThermalComfortBot integrates data from BIM, sensors, and other relevant sources. Built on open-source technology, it is cost-effective and fully customizable, allowing users to tailor datasets to their needs. The Chatbot delivers actionable insights through a Question-Answering (QA) interface, enabling data-driven decisions on thermal comfort to improve workplace conditions and enhance operational efficiency.

Presentation Conference Type Conference Paper (published)
Conference Name 2024 International Conference on Decision Aid Sciences and Applications
Start Date Dec 11, 2024
End Date Dec 12, 2024
Acceptance Date Nov 23, 2024
Deposit Date Nov 28, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Public URL https://uwe-repository.worktribe.com/output/13470785