Prediction of Residential Building Energy Efficiency Performance using Deep Neural Network

One of the important discussions currently in
building energy use is the prediction of energy consumption.
To achieve energy savings and reduce environmental impact,
the prediction of energy consumption in buildings is crucial to
improve energy performance. In this paper, an improved
prediction of energy efficiency performance for the heating
load (HL) and cooling load (CL) of residential buildings is
demonstrated. A deep learning method using a deep neural
network (DNN) based on a multilayer feed-forward artificial
neural network (ANN) trained with stochastic gradient descent
using back-propagation was examined. The proposed DNN
method was also compared with a simple multilayer
perceptron (MLP) ANN method. The error performances of
both DNN and ANN methods were also analyzed against
various machine learning algorithms used in previous studies.
The results showed that the proposed DNN method performed
better in terms of error performance for the mean absolute
error (MAE), root mean square error (RMSE), and mean
absolute percentage error (MAPE) values compared with the
other methods. Adequate values of coefficient of determination
(R2) were also obtained for both HL and CL predictions of the
proposed DNN method, an indication of good prediction
performance. Overall, the proposed ANN and DNN methods
proved to outperform the other methods reviewed in this study.
Based on these findings, it was concluded that the proposed
DNN method was statistically a significant approach within the
related research area