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Highlights
Joint Study by BCA, NParks, and NUS on
Co-Located Solar Panels and Green Roofs
13th August 2025
A study by the National Parks Board (NParks), Building and Construction Authority (BCA), and researchers from the National University of Singapore (NUS), demonstrated the technical feasibility of co-locating solar panels and green roofs. The study, led by Associate Professor Stephen Tay, together with team members Ms. Faizatuzzahrah Rahmaniah and Ms. Joyce Lim Hui Min, reported improved solar panel output, enhanced greenery growth, and reduced indoor temperatures through effective use of limited roof spaces. The study contributes to Singapore’s vision of a “Low-Carbon City” and a “City in Nature”. More information on the work can be found here: https://doi.org/10.1016/j.apenergy.2025.126133
21st August 2025
Prof Yan Da from Tsinghua University shared how AI is revolutionising building energy systems by improving energy efficiency, operational performance, and sustainability on Thursday, 21 August 2025, at the SDE4 Forum. He discussed innovative AI applications, including real-time occupancy detection, occupant monitoring, and occupancy prediction, that optimise energy use in complex buildings. The talk addressed challenges such as fluctuating energy demands and the need for occupant-centric controls, sharing insights from real-world case studies. Prof Yan Da also emphasised the importance of combining AI with HVAC expertise to further enhance energy efficiency and system resilience, supporting global sustainability goals. The session was facilitated by Dr Hu Maomao Hu and Associate Professor Adrian Chong.
Leveraging AI for Optimising Building Energy Systems: Practices and Explorations
Comfort GPT: A Smart Thermostat That Learns Your Temperature Preferences
15th August 2025
Assistant Professor Ali Ghahramani and PhD student Chen Kai from the Department of the Built Environment, College of Design and Engineering, National University of Singapore, have developed a smart thermostat algorithm called ComfortGPT.
ComfortGPT is a novel machine learning architecture trained on historical home thermostat interaction data from over 100,000 buildings spanning several years. Unlike traditional machine learning algorithms, ComfortGPT does not require training from scratch. Instead, it utilizes a transformer architecture to identify the best representative model from a library of pre-trained archetype models. As users interact with their thermostats, the system continuously updates itself, automatically preselecting new setpoints as outdoor conditions change. This enables ComfortGPT to be significantly more accurate than legacy models, accurately predicting individual comfort preferences with minimal user interactions and adapting to small changes in preferences over time.
By adjusting temperatures to align with individual comfort levels, ComfortGPT enhances personal comfort while reducing energy consumption in buildings by minimizing overheating and overcooling, supporting sustainable building management through efficient energy use.
For more information, refer to the following link: https://cde.nus.edu.sg/modelling-personal-comfort-a-smart-thermostat-that-learns-what-temperature-you-like/.
Latest from #NUSDBE
SISV 2025 Gold Winners From DBE
NUS Safety and Resilience Research Unit (SaRRU) Public Seminar
Top Performing Learner Award Top Performing Learner Award
Outstanding Young Alumni Award for CDE Alumni Awards 2025
