University of Oulu

Hietaharju, P.; Ruusunen, M.; Leiviskä, K.; Paavola, M. Predictive Optimization of the Heat Demand in Buildings at the City Level. Appl. Sci. 2019, 9, 1994.

Predictive optimization of the heat demand in buildings at the city level

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Author: Hietaharju, Petri1; Ruusunen, Mika1; Leiviskä, Kauko1;
Organizations: 1Control Engineering, Chemical and Environmental Engineering, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2019
Publish Date: 2019-05-22


Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

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Series: Applied sciences
ISSN: 2076-3417
ISSN-E: 2076-3417
ISSN-L: 2076-3417
Volume: 9
Issue: 10
Article number: 1994
DOI: 10.3390/app9101994
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was funded by the Finnish Funding Agency for Innovation, TEKES through the project KLEI (40267/13) and the Academy of Finland through the project SEN2050 (287748).
Academy of Finland Grant Number: 287748
Detailed Information: 287748 (Academy of Finland Funding decision)
Copyright information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (