## 4 Results and Discussion
### ANNs Model  
ANNs are one famous method to predict the energy consumption of buildings. In the current study, the Neural Network toolbox of MATLAB software was used for applying the ANN model. The first step is to design the learning model by choosing the learning algorithm and the number of hidden layers. Multi-layer perceptron (MLP) is a common feed forward network, it contains one or more layers of hidden neurons, as shown in Figure 2. In MLP, the information transfers forward from inputs through hidden nodes to the output nodes. The training of an MLP is generally performed by using a back-propagation algorithm which encompasses forward step and backward step. The network's structure has to be nominated by selecting training approach and hidden layers. In this research, four types of learning algorithms were tested: Bayesian, Levenberg-Marquardt, Scaled conjugate gradient and resilient algorithms. At the same time, the number of hidden layers was examined for 15, 20, 25, and 30 layers. These parameters were tested on four clusters of DECC data. The output of the ANN model for different algorithms are validated using three measures, these measures are (MSE), (MAPE), and (R\({}^{2}\)), also the overall outputs values was taken in account to evaluate the performance of each case. The measures values for different training algorithm are shown in Table 2.  
From Table 2, the best learning algorithm is Bayesian, especially in cases of clusters 2 and 3. The overall measures show good values such as the R-value being 0.85 and 0.90 respectively, and are not too different from the trained outputs and the tested output. Other algorithms show good values in some clusters, but there were large differences between the trained and tested outputs, also there are many negative results of trained outputs. For selection of the hidden layers, same measures used to validate ANN performance as in Table 3. The best results have been obtained in the model with 20 hidden layers for clusters 1, 2, and 3. Actually, Bayesian neural nets are common for solving problems where data is small, as a way to prevent over fitting. Therefore, it shows better performance in the current data of DECC. Table 3 shows the high R value of the model with 20 hidden layers and the close values between train and test samples. Based on the previous finding, the ANNs models for this research select the Bayesian algorithm as training method with 20 hidden layers for training process. Based on the previous investigations, the Bayesian method and 20 hidden layers will form ANNs model to estimate the energy use of accommodation facilities based on DECC. The previous literature shows the high performance of the ANNs model and accurate results comparing to statistical methods. Therefore, many studies use the ANNs model to analysis building energy with wide range of variable. In Figure 4, the outputs of the ANNs models are illustrated for DECC clusters with predicted value of primary energy using the ANNs model. The good results can be seen clearly by comparing to outputs of the regression model in the previous paragraph. Same as in the regression model, the intendant variables are the consumption of electricity, city gas and clean water. These variables are used as inputs to predict the primary energy outputs by using the ANNs model.