Developing an empirical predictive energy-rating model for windows by using Artificial Neural Network

Mahmoud Shakouri, Saeed BANIHASHEMI

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In line with the growing global trend towards energy efficiency in buildings, this paper introduces a predictive model based on an artificial neural network model to rate the performance of windows in terms of saved heating and cooling loads. A four-story building representing the conventional type of residential apartments in Iran for four climates of cold, hot and humid, hot and arid, and temperate was selected for simulation. An artificial neural network model was developed based on ten variables of U-factor, SHGC, emissivity, monthly average dry bulb temperature, monthly average percent humidity, monthly average wind speed, monthly average direct solar radiation, monthly average diffuse solar radiation, orientation, and month as the input variables. The developed ANN model computes the amount of saved heating or cooling loads as a result of using a window with defined parameters. The best architecture of 10–10-1 with MAPE, RMSE, and R2 values of 1.4%, 0.008, and 0.985, respectively showed an acceptable predictive performance of the model. The predictions of this model, in line with the four levels of window performance defined in this paper, which range from excellent performance to weak performance, constitute the final rating of a window. The rated performance of the windows used in this study showed that the performance of a window can vary in cold and hot months, and windows should be rated according to the climate in which they are being used.
Original languageEnglish
Pages (from-to)961-970
Number of pages10
JournalInternational Journal of Green Energy
Volume16
Issue number13
DOIs
Publication statusPublished - 2019

Fingerprint

Neural networks
Solar radiation
Cooling
Heating
Energy efficiency
Loads (forces)
Atmospheric humidity
Temperature

Cite this

@article{4b7e6e261c364c08a94d141c5af0b1c9,
title = "Developing an empirical predictive energy-rating model for windows by using Artificial Neural Network",
abstract = "In line with the growing global trend towards energy efficiency in buildings, this paper introduces a predictive model based on an artificial neural network model to rate the performance of windows in terms of saved heating and cooling loads. A four-story building representing the conventional type of residential apartments in Iran for four climates of cold, hot and humid, hot and arid, and temperate was selected for simulation. An artificial neural network model was developed based on ten variables of U-factor, SHGC, emissivity, monthly average dry bulb temperature, monthly average percent humidity, monthly average wind speed, monthly average direct solar radiation, monthly average diffuse solar radiation, orientation, and month as the input variables. The developed ANN model computes the amount of saved heating or cooling loads as a result of using a window with defined parameters. The best architecture of 10–10-1 with MAPE, RMSE, and R2 values of 1.4{\%}, 0.008, and 0.985, respectively showed an acceptable predictive performance of the model. The predictions of this model, in line with the four levels of window performance defined in this paper, which range from excellent performance to weak performance, constitute the final rating of a window. The rated performance of the windows used in this study showed that the performance of a window can vary in cold and hot months, and windows should be rated according to the climate in which they are being used.",
keywords = "Window, energy rating system, artificial neural network, cooling load, heating load",
author = "Mahmoud Shakouri and Saeed BANIHASHEMI",
year = "2019",
doi = "10.1080/15435075.2012.738451",
language = "English",
volume = "16",
pages = "961--970",
journal = "International Journal of Green Energy",
issn = "1543-5075",
publisher = "Taylor and Francis Ltd.",
number = "13",

}

Developing an empirical predictive energy-rating model for windows by using Artificial Neural Network. / Shakouri, Mahmoud; BANIHASHEMI, Saeed.

In: International Journal of Green Energy, Vol. 16, No. 13, 2019, p. 961-970.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Developing an empirical predictive energy-rating model for windows by using Artificial Neural Network

AU - Shakouri, Mahmoud

AU - BANIHASHEMI, Saeed

PY - 2019

Y1 - 2019

N2 - In line with the growing global trend towards energy efficiency in buildings, this paper introduces a predictive model based on an artificial neural network model to rate the performance of windows in terms of saved heating and cooling loads. A four-story building representing the conventional type of residential apartments in Iran for four climates of cold, hot and humid, hot and arid, and temperate was selected for simulation. An artificial neural network model was developed based on ten variables of U-factor, SHGC, emissivity, monthly average dry bulb temperature, monthly average percent humidity, monthly average wind speed, monthly average direct solar radiation, monthly average diffuse solar radiation, orientation, and month as the input variables. The developed ANN model computes the amount of saved heating or cooling loads as a result of using a window with defined parameters. The best architecture of 10–10-1 with MAPE, RMSE, and R2 values of 1.4%, 0.008, and 0.985, respectively showed an acceptable predictive performance of the model. The predictions of this model, in line with the four levels of window performance defined in this paper, which range from excellent performance to weak performance, constitute the final rating of a window. The rated performance of the windows used in this study showed that the performance of a window can vary in cold and hot months, and windows should be rated according to the climate in which they are being used.

AB - In line with the growing global trend towards energy efficiency in buildings, this paper introduces a predictive model based on an artificial neural network model to rate the performance of windows in terms of saved heating and cooling loads. A four-story building representing the conventional type of residential apartments in Iran for four climates of cold, hot and humid, hot and arid, and temperate was selected for simulation. An artificial neural network model was developed based on ten variables of U-factor, SHGC, emissivity, monthly average dry bulb temperature, monthly average percent humidity, monthly average wind speed, monthly average direct solar radiation, monthly average diffuse solar radiation, orientation, and month as the input variables. The developed ANN model computes the amount of saved heating or cooling loads as a result of using a window with defined parameters. The best architecture of 10–10-1 with MAPE, RMSE, and R2 values of 1.4%, 0.008, and 0.985, respectively showed an acceptable predictive performance of the model. The predictions of this model, in line with the four levels of window performance defined in this paper, which range from excellent performance to weak performance, constitute the final rating of a window. The rated performance of the windows used in this study showed that the performance of a window can vary in cold and hot months, and windows should be rated according to the climate in which they are being used.

KW - Window

KW - energy rating system

KW - artificial neural network

KW - cooling load

KW - heating load

U2 - 10.1080/15435075.2012.738451

DO - 10.1080/15435075.2012.738451

M3 - Article

VL - 16

SP - 961

EP - 970

JO - International Journal of Green Energy

JF - International Journal of Green Energy

SN - 1543-5075

IS - 13

ER -