Buildings Energy Prediction Using Artificial Neural Networks | ||||
| Engineering Research Journal | ||||
| Article 7, Volume 171, Issue 0, September 2021, Page 106-118 PDF (624.36 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erj.2021.193803 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mahmoud Abdelkader Bashery Abbass* 1; Hatem Sadek1; Mohamed Hamdy2 | ||||
| 1Helwan University, Department of Mechanical Power Engineering, Cairo, Egypt | ||||
| 2Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway | ||||
| Abstract | ||||
| 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. | ||||
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