IDEAS Lab

Integrated Data, Energy Analysis + Simulation

A Passion for Better Buildings

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IDEAS Lab is a research group in the Department of Building at the National University of Singapore (NUS) that engages in the integration building performance simulation with real-time data and machine learning. The group focuses on various aspects of building performance simulation including uncertainty quantification, model calibration, and building data analytics with the aim of creating energy efficient and healthier buildings.

Also, check out our two sibling labs: the Building and Urban Data Science (BUDS) Lab and the Urban Analytics Lab (UAL), with whom we collaborate closely within NUS. .

Projects

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COVID-19: Rethinking building design and operations

Experiments and CFD studies for droplet and bioaerosol transmission inside building. A project funded by NUS COVID-19 Research Seed Grant.

Advanced machine learning based control for HVAC systems

Advanced machine learning based control for the HVAC systems

An automated framework for model predictive control

This study aims at improving the scalability of MPC by addressing modeling-related issues and establishing an automated framework.

Building lifecycle automatic data segmentation using natural language processing

Building lifecycle automatic data segmentation using natural language processing

Epwshiftr: an R package for creating future weather files under climate changes for building energy simulation

An R package for creating future weather files under climate changes for building energy simulation

Impacts of climate change on building energy systems

Impacts of climate change on building energy systems

Life cycle cost optimization

Life cycle cost optimization

Quantifying the impact of occupancy on ECMs’ performance using building energy simulation

A framework for identifying and quantifying the impact of occupancy-related schedules on ECMs’ performance using building energy simulation

eplusr: A framework for integrating building energy simulation and data-driven analytics

A framework for seamless integration between Building Energy Simulation (BES) and data-driven analytics.

Generating certified energy models in Singapore through an M&V framework.

Generating certified energy models in Singapore through an M&V framework.An NUS project in collaboration with Professor Godfried L. Augenbroe (Georgia Institute of Technology)

Postdoctoral Researchers

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Hongyuan Jia

Postdoctoral Fellow

Building Energy Simulation, Data Analytics, R Programming

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Wenxin Li

Postdoctoral Fellow

Indoor Air Quality, CFD Simulation, Data Assimilation

PhD Students / Research Assistants

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Shuxu Qin

Research Apprentice

Community/Urban Energy Planning, Sustainable Planning and Design, Human Wellness Solution and Design, Data Analytics

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Sicheng Zhan

PhD Student

Building Energy Simulation, Building Data Analytics, Model Predictive Control

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Yaonan (Claire) Gu

PhD Student

Building Energy Simulation, Bayesian Calibration, Data Analytics and Applied Machine Learning

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Yue Lei

PhD Student

Building Performance Modelling and Optimization, Model Prodictive Control (MPC), Indoor CFD Application, Data Analytics

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Siyu Cheng

PhD Student

Building Performance Modelling and Optimization

Contact

  • +65 98210638
  • Department of Building, School of Design and Environment, National University of Singapore, Singapore
  • email

Recent Publications

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Model predictive control (MPC) has shown great potential in improving building performance and saving energy. However, after over 20 …

Occupancy is a significant area of interest within the field of building performance simulation. Through Bayesian calibration, the …

Building energy simulation (BES) has been widely adopted for the investigation of building environmental and energy performance for …

Current building energy benchmarking systems categorize buildings into peer groups by static characteristics such as climate zones and …