Model-based and data-driven approaches for building automation and control

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Wei, T; Chen, X; Li, X; Zhu, Q

Published Date

  • November 5, 2018

Published In

International Standard Serial Number (ISSN)

  • 1092-3152

International Standard Book Number 13 (ISBN-13)

  • 9781450359504

Digital Object Identifier (DOI)

  • 10.1145/3240765.3243485

Citation Source

  • Scopus