Nonlinear model predictive control for the space heating system of a university building in Norway

Juan Hou, Haoran Li and Natasa Nord

Energy, 2022, vol. 253, issue C

Abstract: Space heating accounts for a significant share of a building's energy use; moreover, occupants' desires for better thermal comfort may further increase the energy use and peak load of the space heating system, as well as its total energy cost. The goal of this study was to use an optimal control technique: model-based predictive controller, to improve the energy and economic performance of space heating systems with satisfying indoor temperature. The proposed optimal controller incorporated the dynamic energy prices, the disturbances from weather and occupancy, as well as a nonlinear system dynamic model that considered the building thermal mass as thermal energy storage. The model-based predictive controller was tested by simulation on a university building in central Norway, and its control performance was compared to a conventional rule-based control approach, which is currently employed by the case system. The model-based predictive controller's effectiveness was demonstrated by a 2.8% reduction in heat use, 8.8% shaving in peak load, 3.8% saving in heating cost, and a 59% drop in indoor temperature violation. Furthermore, a sensitivity study revealed that the model-based predictive controller still maintained high energy and economic performance even under lightweight building constructions with decreased building thermal mass.

Keywords: Thermal energy storage; Peak load; Heating cost; Indoor temperature; Optimal control (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422201060X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:253:y:2022:i:c:s036054422201060x

DOI: 10.1016/j.energy.2022.124157

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().