In the current state of the art load management and demand response actions in smart buildings are often predetermined by a field engineer to a fixed set of (rule-based) options. This fixed set of options often neglects the cyberphysical nature of the building dynamics, thermostatic action and building automation system. In this work we will combine a rule-based load management program with a learning feedback load management program that can operate on top of the rules. We demonstrate via extensive simulations the effectiveness of the program for intelligent management of the heating, ventilating and air conditioning (HVAC) loads so as to exploit renewable energy sources, while taking into account humanrelated
constraints like thermal comfort.
Original languageEnglish
Title of host publicationProceedings of the 2017 13th IEEE International Conference on Control & Automation (ICCA)
EditorsL. Liu, H. Lin
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages224-229
ISBN (Print)978-1-5386-2679-5
DOIs
Publication statusPublished - 2017
EventICCA 2017 13th International Conference on Control & Automation - Ohrid, Macedonia, The Former Yugoslav Republic of
Duration: 3 Jul 20176 Jul 2017

Conference

ConferenceICCA 2017 13th International Conference on Control & Automation
Abbreviated titleICCA 2017
CountryMacedonia, The Former Yugoslav Republic of
CityOhrid
Period3/07/176/07/17

    Research areas

  • Demand-side management, Thermal comfort optimization, Occupancy information

ID: 31361663