Determining adaptability performance of artificial neural network-based thermal control logics for envelope conditions in residential buildings

Jin Woo Moon, Jae D. Chang, Sooyoung Kim

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics.

Original languageEnglish
Pages (from-to)3548-3570
Number of pages23
JournalEnergies
Volume6
Issue number7
DOIs
Publication statusPublished - 2013 Jan 1

Fingerprint

Thermal Control
Adaptability
Envelope
Artificial Neural Network
Logic
Neural networks
Predictive Control
Overshoot
Neural Networks
Performance Test
Thermal Properties
Humidity
Simulation Software
Neural Network Model
Range of data
Percentage
Control Strategy
Thermal comfort
Buildings
Hot Temperature

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

@article{aaf715ab21e44744909bfa3caef4d20c,
title = "Determining adaptability performance of artificial neural network-based thermal control logics for envelope conditions in residential buildings",
abstract = "This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics.",
author = "Moon, {Jin Woo} and Chang, {Jae D.} and Sooyoung Kim",
year = "2013",
month = "1",
day = "1",
doi = "10.3390/en6073548",
language = "English",
volume = "6",
pages = "3548--3570",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "7",

}

Determining adaptability performance of artificial neural network-based thermal control logics for envelope conditions in residential buildings. / Moon, Jin Woo; Chang, Jae D.; Kim, Sooyoung.

In: Energies, Vol. 6, No. 7, 01.01.2013, p. 3548-3570.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Determining adaptability performance of artificial neural network-based thermal control logics for envelope conditions in residential buildings

AU - Moon, Jin Woo

AU - Chang, Jae D.

AU - Kim, Sooyoung

PY - 2013/1/1

Y1 - 2013/1/1

N2 - This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics.

AB - This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics.

UR - http://www.scopus.com/inward/record.url?scp=84881496039&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84881496039&partnerID=8YFLogxK

U2 - 10.3390/en6073548

DO - 10.3390/en6073548

M3 - Article

VL - 6

SP - 3548

EP - 3570

JO - Energies

JF - Energies

SN - 1996-1073

IS - 7

ER -