This paper aims to prove that the artificial neural network (ANN) is a
powerful tool in prediction of buildings energy consumption, this target is achieved by
comparing the accuracy of ANN prediction with the output of simple linear regression
algorithm and previous work. First of all, the flowchart depends on four main steps: 1)
Data selection, 2) Data preparation, 3) Model training and tuning, and 4) Evaluate results.
The Commercial Buildings Energy Consumption Survey (CBECS) is selected as a data set
to apply ANN on it by choosing the most effective features that have the main influence on
the energy consumption. Data preparation process is done by replacing missing values and
outliers’ values wi th median value of each feature. The model’s hyper-parameters are
tuned by manual method depending on the author expeience of ANN algorithm and the
evaluation step done by using mean absolute error (MAE), mean square error (MSE), root
mean square error (RMSE) and r-squared value as a metric for performance. The results
showed that the proposed ANN algorithm achives high performance comparing to simple
linear regression algorithm and previous work on the same data.
Abdelkader Bashery Abbass, M., Sadek, H., & Hamdy, M. (2021). Buildings Energy Prediction Using Artificial Neural Networks. Engineering Research Journal, 171(0), 106-118. doi: 10.21608/erj.2021.193803
MLA
Mahmoud Abdelkader Bashery Abbass; Hatem Sadek; Mohamed Hamdy. "Buildings Energy Prediction Using Artificial Neural Networks". Engineering Research Journal, 171, 0, 2021, 106-118. doi: 10.21608/erj.2021.193803
HARVARD
Abdelkader Bashery Abbass, M., Sadek, H., Hamdy, M. (2021). 'Buildings Energy Prediction Using Artificial Neural Networks', Engineering Research Journal, 171(0), pp. 106-118. doi: 10.21608/erj.2021.193803
VANCOUVER
Abdelkader Bashery Abbass, M., Sadek, H., Hamdy, M. Buildings Energy Prediction Using Artificial Neural Networks. Engineering Research Journal, 2021; 171(0): 106-118. doi: 10.21608/erj.2021.193803
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