Indoor Climate Modelling and Optimal Planning With Respect To Electricity Prices

Shaojun Huang, Konstantin Filonenko, Yuming Zhao, Tao Yang, Tianlong Xiong, Christian Veje

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

108 Downloads (Pure)

Abstract

This paper proposes an optimal energy planning method for minimizing the cost of heating, ventilation, and air conditioning (HVAC) of a building. Firstly, an RC (Resistance-Capacitance) state space model which can describe the thermal and CO2 dynamics of a building is established. Its parameters can be estimated based on time-series measurements: solar radiation, outside temperature, room temperature, number of occupants, ventilation rate, heating supply, CO2 level, etc. Secondly, this state space model in continuous time domain is rearranged and discretized. Thirdly, the discretized model is converted to constraints and an energy planning method based on linear programming is made for minimizing the costs of CO2 level and room temperature controls. Finally, case studies of a teaching building are carried out to demonstrate the efficacy of the proposed optimal planning method.
Original languageEnglish
Title of host publication2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Number of pages5
PublisherIEEE
Publication date2021
ISBN (Electronic)978-1-6654-0507-2
DOIs
Publication statusPublished - 2021
EventIEEE Power & Energy Society General Meeting, PESGM 2021 - Washington D.C., United States
Duration: 26. Jul 202129. Jul 2021

Conference

ConferenceIEEE Power & Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington D.C.
Period26/07/202129/07/2021
SeriesIEEE Power and Energy Society General Meeting
ISSN1944-9925

Keywords

  • Building climate control
  • RC model
  • heating ventilation and air conditioning (HVAC)
  • linear programming
  • optimal energy planning

Fingerprint

Dive into the research topics of 'Indoor Climate Modelling and Optimal Planning With Respect To Electricity Prices'. Together they form a unique fingerprint.

Cite this