Multi-zone temperature prediction in a commercial building using artificial neural network model

Hao Huang, Lei Chen, Morteza Mohammadzaheri, Eric Hu, Minlei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.

Original languageEnglish
Title of host publication2013 10th IEEE International Conference on Control and Automation, ICCA 2013
Pages1896-1901
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 10th IEEE International Conference on Control and Automation, ICCA 2013 - Hangzhou, China
Duration: Jun 12 2013Jun 14 2013

Other

Other2013 10th IEEE International Conference on Control and Automation, ICCA 2013
CountryChina
CityHangzhou
Period6/12/136/14/13

Fingerprint

Neural networks
Model predictive control
Air conditioning
Temperature
Ventilation
Heating
Uncertainty
Hot Temperature

Keywords

  • Artificial neural network
  • HVAC
  • Model predictive control
  • Multi-zone

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

Cite this

Huang, H., Chen, L., Mohammadzaheri, M., Hu, E., & Chen, M. (2013). Multi-zone temperature prediction in a commercial building using artificial neural network model. In 2013 10th IEEE International Conference on Control and Automation, ICCA 2013 (pp. 1896-1901). [6565010] https://doi.org/10.1109/ICCA.2013.6565010

Multi-zone temperature prediction in a commercial building using artificial neural network model. / Huang, Hao; Chen, Lei; Mohammadzaheri, Morteza; Hu, Eric; Chen, Minlei.

2013 10th IEEE International Conference on Control and Automation, ICCA 2013. 2013. p. 1896-1901 6565010.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Huang, H, Chen, L, Mohammadzaheri, M, Hu, E & Chen, M 2013, Multi-zone temperature prediction in a commercial building using artificial neural network model. in 2013 10th IEEE International Conference on Control and Automation, ICCA 2013., 6565010, pp. 1896-1901, 2013 10th IEEE International Conference on Control and Automation, ICCA 2013, Hangzhou, China, 6/12/13. https://doi.org/10.1109/ICCA.2013.6565010
Huang H, Chen L, Mohammadzaheri M, Hu E, Chen M. Multi-zone temperature prediction in a commercial building using artificial neural network model. In 2013 10th IEEE International Conference on Control and Automation, ICCA 2013. 2013. p. 1896-1901. 6565010 https://doi.org/10.1109/ICCA.2013.6565010
Huang, Hao ; Chen, Lei ; Mohammadzaheri, Morteza ; Hu, Eric ; Chen, Minlei. / Multi-zone temperature prediction in a commercial building using artificial neural network model. 2013 10th IEEE International Conference on Control and Automation, ICCA 2013. 2013. pp. 1896-1901
@inproceedings{00c11f44aaa64980bffb504b8b214cac,
title = "Multi-zone temperature prediction in a commercial building using artificial neural network model",
abstract = "Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.",
keywords = "Artificial neural network, HVAC, Model predictive control, Multi-zone",
author = "Hao Huang and Lei Chen and Morteza Mohammadzaheri and Eric Hu and Minlei Chen",
year = "2013",
doi = "10.1109/ICCA.2013.6565010",
language = "English",
isbn = "9781467347075",
pages = "1896--1901",
booktitle = "2013 10th IEEE International Conference on Control and Automation, ICCA 2013",

}

TY - GEN

T1 - Multi-zone temperature prediction in a commercial building using artificial neural network model

AU - Huang, Hao

AU - Chen, Lei

AU - Mohammadzaheri, Morteza

AU - Hu, Eric

AU - Chen, Minlei

PY - 2013

Y1 - 2013

N2 - Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.

AB - Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.

KW - Artificial neural network

KW - HVAC

KW - Model predictive control

KW - Multi-zone

UR - http://www.scopus.com/inward/record.url?scp=84882321303&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84882321303&partnerID=8YFLogxK

U2 - 10.1109/ICCA.2013.6565010

DO - 10.1109/ICCA.2013.6565010

M3 - Conference contribution

SN - 9781467347075

SP - 1896

EP - 1901

BT - 2013 10th IEEE International Conference on Control and Automation, ICCA 2013

ER -