Abstract
Smart buildings in the future are complex cyber-physical-human systems that involve close interactions among embedded platform (for sensing, computation, communication and control), mechanical components, physical environment, building architecture, and occupant activities. The design and operation of such buildings require a new set of methodologies and tools that can address these heterogeneous domains in a holistic, quantitative and automated fashion. In this paper, we will present our design automation methods for improving building energy efficiency and offering comfortable services to occupants at low cost. In particular, we will highlight our work in developing both model-based and data-driven approaches for building automation and control, including methods for co-scheduling heterogeneous energy demands and supplies, for integrating intelligent building energy management with grid optimization through a proactive demand response framework, for optimizing HVAC control with deep reinforcement learning, and for accurately measuring in-building temperature by combining prior modeling information with few sensor measurements based upon Bayesian inference.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781450359504 |
| DOIs | |
| State | Published - Nov 5 2018 |
| Event | 37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - San Diego, United States Duration: Nov 5 2018 → Nov 8 2018 |
Publication series
| Name | IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD |
|---|---|
| ISSN (Print) | 1092-3152 |
Other
| Other | 37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 11/5/18 → 11/8/18 |
Fingerprint
Keywords
- Bayesian inference
- data-driven
- deep reinforcement learning
- model predictive control
- model-based design
- smart buildings
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
Cite this
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Model-based and data-driven approaches for building automation and control. / Wei, Tianshu; Chen, Xiaoming; Li, Xin; Zhu, Qi.
2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc., 2018. a26 (IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Model-based and data-driven approaches for building automation and control
AU - Wei, Tianshu
AU - Chen, Xiaoming
AU - Li, Xin
AU - Zhu, Qi
PY - 2018/11/5
Y1 - 2018/11/5
N2 - Smart buildings in the future are complex cyber-physical-human systems that involve close interactions among embedded platform (for sensing, computation, communication and control), mechanical components, physical environment, building architecture, and occupant activities. The design and operation of such buildings require a new set of methodologies and tools that can address these heterogeneous domains in a holistic, quantitative and automated fashion. In this paper, we will present our design automation methods for improving building energy efficiency and offering comfortable services to occupants at low cost. In particular, we will highlight our work in developing both model-based and data-driven approaches for building automation and control, including methods for co-scheduling heterogeneous energy demands and supplies, for integrating intelligent building energy management with grid optimization through a proactive demand response framework, for optimizing HVAC control with deep reinforcement learning, and for accurately measuring in-building temperature by combining prior modeling information with few sensor measurements based upon Bayesian inference.
AB - Smart buildings in the future are complex cyber-physical-human systems that involve close interactions among embedded platform (for sensing, computation, communication and control), mechanical components, physical environment, building architecture, and occupant activities. The design and operation of such buildings require a new set of methodologies and tools that can address these heterogeneous domains in a holistic, quantitative and automated fashion. In this paper, we will present our design automation methods for improving building energy efficiency and offering comfortable services to occupants at low cost. In particular, we will highlight our work in developing both model-based and data-driven approaches for building automation and control, including methods for co-scheduling heterogeneous energy demands and supplies, for integrating intelligent building energy management with grid optimization through a proactive demand response framework, for optimizing HVAC control with deep reinforcement learning, and for accurately measuring in-building temperature by combining prior modeling information with few sensor measurements based upon Bayesian inference.
KW - Bayesian inference
KW - data-driven
KW - deep reinforcement learning
KW - model predictive control
KW - model-based design
KW - smart buildings
UR - http://www.scopus.com/inward/record.url?scp=85058156512&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058156512&partnerID=8YFLogxK
U2 - 10.1145/3240765.3243485
DO - 10.1145/3240765.3243485
M3 - Conference contribution
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - 2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers
PB - Institute of Electrical and Electronics Engineers Inc.
ER -