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Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

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@article{IJASEIT7351,
   author = {Alaa Abdulhady Jaber and Ahmed Saleh and Hussein Fouad Mohammed Ali},
   title = {Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {1},
   year = {2019},
   pages = {159--166},
   keywords = {cooling energy; artificial neural network; HAP software; energy management},
   abstract = {Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system.  Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7351},
   doi = {10.18517/ijaseit.9.1.7351}
}

EndNote

%A Jaber, Alaa Abdulhady
%A Saleh, Ahmed
%A Mohammed Ali, Hussein Fouad
%D 2019
%T Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network
%B 2019
%9 cooling energy; artificial neural network; HAP software; energy management
%! Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network
%K cooling energy; artificial neural network; HAP software; energy management
%X Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system.  Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7351
%R doi:10.18517/ijaseit.9.1.7351
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 1
%@ 2088-5334

IEEE

Alaa Abdulhady Jaber,Ahmed Saleh and Hussein Fouad Mohammed Ali,"Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 1, pp. 159-166, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.1.7351.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Jaber, Alaa Abdulhady
AU  - Saleh, Ahmed
AU  - Mohammed Ali, Hussein Fouad
PY  - 2019
TI  - Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 1
Y2  - 2019
SP  - 159
EP  - 166
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - cooling energy; artificial neural network; HAP software; energy management
N2  - Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system.  Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7351
DO  - 10.18517/ijaseit.9.1.7351

RefWorks

RT Journal Article
ID 7351
A1 Jaber, Alaa Abdulhady
A1 Saleh, Ahmed
A1 Mohammed Ali, Hussein Fouad
T1 Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 1
YR 2019
SP 159
OP 166
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 cooling energy; artificial neural network; HAP software; energy management
AB Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system.  Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7351
DO  - 10.18517/ijaseit.9.1.7351