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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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.

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

Life cycle cost optimization

Life cycle cost optimization

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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Mingya Zhu

Postdoctoral Fellow

Model calibration/validation, Building Energy Performance Simulation, Occupant behavior models, District energy demand prediction

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Xilei Dai

Postdoctoral Fellow

Deep learning, Indoor air quality, Building modelling and simulation

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Yuzhen Peng

Senior Postdoctoral Fellow

Smart buildings and systems, Automation and controls, Machine learning and optimization, Data-driven modeling, User-centric design, IoT and sensing platforms, Human-machine interfaces

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Zeynep Duygu Tekler

Postdoctoral Fellow

IoT Applications, Occupancy Detection, Smart Energy Management Systems, Data Analytics and Applied Machine Learning

PhD Students / Research Assistants

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Jintong Han

PhD Student

Continual Learning, Physical-based Deep Learning

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Long Zheng

PhD Student

Computational Fluid Dynamics, Convective Heat transfer, Microclimate

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Sicheng Zhan

PhD Student

Building Energy Simulation, Building Data Analytics, Model Predictive Control

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Siyu Cheng

PhD Student

Building Performance Modelling and Optimization

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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

Alumni

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Hongyuan Jia

Assistant Professor (Chongqing University of Science and Technology)

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Wenxin Li

Assistant Professor (Southeast Univesity, China)

Contact

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

Recent Publications

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Renewable energy usage is continuing to increase as many countries worldwide are aiming to reach peak carbon emission and achieve …

Reinforcement learning (RL) has been shown to have the potential for optimal control of heating, ventilation, and air conditioning …

The paper presents a review on major contributions in infrared thermography to study the built environment at multiple scales. To …

Model predictive control (MPC) has shown potential in improving building performance but is bottlenecked by the difficulty in …

Incorporating data-driven thermal comfort models into occupant-centric HVAC controls is crucial to meet occupants’ preferences in …