Abstract
An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters - four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash-Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%. (author)
Wong, S L;
Wan, Kevin K.W.;
Lam, Tony N.T.
[1]
- Building Energy Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR (China)
Citation Formats
Wong, S L, Wan, Kevin K.W., and Lam, Tony N.T.
Artificial neural networks for energy analysis of office buildings with daylighting.
United Kingdom: N. p.,
2010.
Web.
doi:10.1016/J.APENERGY.2009.06.028.
Wong, S L, Wan, Kevin K.W., & Lam, Tony N.T.
Artificial neural networks for energy analysis of office buildings with daylighting.
United Kingdom.
https://doi.org/10.1016/J.APENERGY.2009.06.028
Wong, S L, Wan, Kevin K.W., and Lam, Tony N.T.
2010.
"Artificial neural networks for energy analysis of office buildings with daylighting."
United Kingdom.
https://doi.org/10.1016/J.APENERGY.2009.06.028.
@misc{etde_21235618,
title = {Artificial neural networks for energy analysis of office buildings with daylighting}
author = {Wong, S L, Wan, Kevin K.W., and Lam, Tony N.T.}
abstractNote = {An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters - four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash-Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%. (author)}
doi = {10.1016/J.APENERGY.2009.06.028}
journal = []
issue = {2}
volume = {87}
place = {United Kingdom}
year = {2010}
month = {Feb}
}
title = {Artificial neural networks for energy analysis of office buildings with daylighting}
author = {Wong, S L, Wan, Kevin K.W., and Lam, Tony N.T.}
abstractNote = {An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters - four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash-Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%. (author)}
doi = {10.1016/J.APENERGY.2009.06.028}
journal = []
issue = {2}
volume = {87}
place = {United Kingdom}
year = {2010}
month = {Feb}
}