Year: 2024
Type: conference-paper
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.
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406521680