A dynamic model for indoor temperature prediction in buildings
|Author:||Hietaharju, Petri1; Ruusunen, Mika1; Leiviskä, Kauko1|
1Control Engineering, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018061425862
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2018-06-14
A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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 (Academy of Finland Funding decision)
© 2018 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 (http://creativecommons.org/licenses/by/4.0/).