Cargando…

Application of artificial neural network for natural ventilation schemes to control operable windows

An artificial neural network (ANN) has been broadly developed as a design tool in various application scenarios in building sectors. One of the most important perspectives in building fields is human comfort. Various control strategies of natural ventilation schemes exist, to maintain good air quali...

Descripción completa

Detalles Bibliográficos
Autores principales: Srisamranrungruang, Thanyalak, Hiyama, Kyosuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694107/
https://www.ncbi.nlm.nih.gov/pubmed/36439739
http://dx.doi.org/10.1016/j.heliyon.2022.e11817
_version_ 1784837715916750848
author Srisamranrungruang, Thanyalak
Hiyama, Kyosuke
author_facet Srisamranrungruang, Thanyalak
Hiyama, Kyosuke
author_sort Srisamranrungruang, Thanyalak
collection PubMed
description An artificial neural network (ANN) has been broadly developed as a design tool in various application scenarios in building sectors. One of the most important perspectives in building fields is human comfort. Various control strategies of natural ventilation schemes exist, to maintain good air quality in buildings. Nevertheless, this study presented a novel strategy by applying a simple ANN to predict the trends of indoor air temperature and determine the operation status of operable windows. Building simulations had been conducted to train, test, and validate the ANN model. The ANN model has one hidden layer and performs training using the Levenberg-Marquardt algorithm. The nodes in the hidden layer were varied to configure the best-fitting model. The best structure of the ANN model in this study is the model with one hidden layer and 20 nodes. This study compares the significance of adopting a data set between differential data with time series and raw data. The application of the differential data set exhibits better performance in predicting the indoor air temperature increase or decrease than that of the raw data. The prediction precision between the simulation and the ANN model when adopting the differential data is higher than that of raw data by 18%. This study discovered a new simple method and verified that a simple control strategy has been achieved by predicting the window operations using the increase or decrease in indoor temperatures via the ANN application.
format Online
Article
Text
id pubmed-9694107
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-96941072022-11-26 Application of artificial neural network for natural ventilation schemes to control operable windows Srisamranrungruang, Thanyalak Hiyama, Kyosuke Heliyon Research Article An artificial neural network (ANN) has been broadly developed as a design tool in various application scenarios in building sectors. One of the most important perspectives in building fields is human comfort. Various control strategies of natural ventilation schemes exist, to maintain good air quality in buildings. Nevertheless, this study presented a novel strategy by applying a simple ANN to predict the trends of indoor air temperature and determine the operation status of operable windows. Building simulations had been conducted to train, test, and validate the ANN model. The ANN model has one hidden layer and performs training using the Levenberg-Marquardt algorithm. The nodes in the hidden layer were varied to configure the best-fitting model. The best structure of the ANN model in this study is the model with one hidden layer and 20 nodes. This study compares the significance of adopting a data set between differential data with time series and raw data. The application of the differential data set exhibits better performance in predicting the indoor air temperature increase or decrease than that of the raw data. The prediction precision between the simulation and the ANN model when adopting the differential data is higher than that of raw data by 18%. This study discovered a new simple method and verified that a simple control strategy has been achieved by predicting the window operations using the increase or decrease in indoor temperatures via the ANN application. Elsevier 2022-11-22 /pmc/articles/PMC9694107/ /pubmed/36439739 http://dx.doi.org/10.1016/j.heliyon.2022.e11817 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Srisamranrungruang, Thanyalak
Hiyama, Kyosuke
Application of artificial neural network for natural ventilation schemes to control operable windows
title Application of artificial neural network for natural ventilation schemes to control operable windows
title_full Application of artificial neural network for natural ventilation schemes to control operable windows
title_fullStr Application of artificial neural network for natural ventilation schemes to control operable windows
title_full_unstemmed Application of artificial neural network for natural ventilation schemes to control operable windows
title_short Application of artificial neural network for natural ventilation schemes to control operable windows
title_sort application of artificial neural network for natural ventilation schemes to control operable windows
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694107/
https://www.ncbi.nlm.nih.gov/pubmed/36439739
http://dx.doi.org/10.1016/j.heliyon.2022.e11817
work_keys_str_mv AT srisamranrungruangthanyalak applicationofartificialneuralnetworkfornaturalventilationschemestocontroloperablewindows
AT hiyamakyosuke applicationofartificialneuralnetworkfornaturalventilationschemestocontroloperablewindows