research-article

Model-based and Data-driven Approaches for Building Automation and Control

Authors:

Qi ZhuAuthors Info & Claims

2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Pages 1 - 8

https://doi.org/10.1145/3240765.3243485

Published: 05 November 2018 Publication History

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.

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cover image Guide Proceedings

2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Nov 2018

939 pages

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IEEE Press

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Published: 05 November 2018

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Cited By

van Roosmale SHellinckx PMeysman JVerbeke SAudenaert A(2024)Building Automation and Control Systems for Office Buildings: Technical Insights for Effective Facility Management - A Literature ReviewJournal of Building Engineering10.1016/j.jobe.2024.110943(110943)Online publication date: Oct-2024
https://doi.org/10.1016/j.jobe.2024.110943
Sierla SIhasalo HVyatkin V(2022)A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning SystemsEnergies10.3390/en1510352615:10(3526)Online publication date: 11-May-2022
https://doi.org/10.3390/en15103526
Pan KPalensky PEsfahani P(2022)Dynamic Anomaly Detection With High-Fidelity Simulators: A Convex Optimization ApproachIEEE Transactions on Smart Grid10.1109/TSG.2021.312907413:2(1500-1515)Online publication date: Mar-2022
https://doi.org/10.1109/TSG.2021.3129074
Kou XDu YLi FPulgar-Painemal HZandi HDong JOlama M(2021)Model-Based and Data-Driven HVAC Control Strategies for Residential Demand ResponseIEEE Open Access Journal of Power and Energy10.1109/OAJPE.2021.30754268(186-197)Online publication date: 2021
https://doi.org/10.1109/OAJPE.2021.3075426
Yu LQin SShen C(2021)Optimal Operation of a Hydrogen-based Building Multi-Energy System under Uncertainties2021 China Automation Congress (CAC)10.1109/CAC53003.2021.9727527(1583-1588)Online publication date: 22-Oct-2021
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Lissa PDeane CSchukat MSeri FKeane MBarrett E(2021)Deep reinforcement learning for home energy management system controlEnergy and AI10.1016/j.egyai.2020.1000433(100043)Online publication date: Mar-2021
https://doi.org/10.1016/j.egyai.2020.100043
Faddel STian GZhou QAburub H(2020)Data Driven Q-Learning for Commercial HVAC Control2020 SoutheastCon10.1109/SoutheastCon44009.2020.9249737(1-6)Online publication date: 28-Mar-2020
https://doi.org/10.1109/SoutheastCon44009.2020.9249737

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