## 6 Forecasting Results and Discussion
### Comparison with Conventional Methods  
Due to many promising results of deep learning-based load forecasting models [37], we should benchmark the results of our proposed model against conventional machine learning methods such as support vector regression (SVR) and decision tree (DT). Additionally, we should benchmark the results against conventional deep learning methods such as artificial neural networks (ANN) and conventional LSTM to explicate the out-performance of our proposed method. Nevertheless, we should compare our proposed model to a hybrid model CNN-LSTM. The comparison methods preferred in the study are selected according to the rule of reproducible and interpretability. The parameter settings of SVR and DT models are determined according to the user guides of scikit-learn in [38; 39], respectively. The rest of the comparison models parameter settings are presented in Table 2.  
In the proposed work, we investigated several machine learning and deep learning models to find the optimal short-term load forecasting. First, we performed experiments over machine learning models that include SVR and DT. The performance of the SVR model is quite good in the average MAPE compared to other deep learning models. However, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Design Parameters of the Comparison Methods \({}^{\star}\)**} \\ \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**N. of Hidden Neuron**} & \multirow{2}{*}{**Activation Function**} & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**Filter Size**} & \multirow{2}{*}{**Pool Size**} & \multirow{2}{*}{**Optimizer**} \\  & & & & & & \\ \hline ANN & 500 & sigmoid & 500 & - & - & SGD \({}^{\star\star}\) \\ CNN & - & ReLU & 200 & 64 & - & ADAM \\ LSTM & 200 & sigmoid & 200 & - & - & SGD \\ BLSTM & 50 & - & 200 & - & - & ADAM \\ CNN-LSTM & 50/25 & ReLU & 200 & 64 & 1 & ADAM \\ \hline \hline \end{tabular}  
* The epoch and loss function are set to 100 and MSE, respectively, for all models. \({}^{\star\star}\) stochastic gradient descent (SGD).  
\end{table} 
Table 2: Parameter settings of comparison models. the DT model did not perform well for all the evaluation metrics. For instance, the average MAPE results of the machine learning models are 55.83% and 141.067 % for SVR and DT, respectively. However, these machine learning models did not perform well in the other evaluation metrics such as RMSE, NRMSE, and MAE. From Table 3, we can see that simple machine learning models lacks tackling the behavior of energy time-series data. After extensive experiments on machine learning models and observing their performance with energy time series data, we performed experiments over several deep learning models including ANN, CNN, LSTM, BLSTM, and CNN-LSTM. From the experiments, the ANN model performed better than previous machine learning models such as SVR and DT in all evaluation metrics while the CNN, LSTM, BLSTM, CNN-LSTM models performed relatively close to each other. The proposed CNN-BLSTM model showed better performance in all evaluation metrics, with 0.108, 55.43, 0.058, and 36.75 RMSE, NRMSE, MAE, and MAPE, respectively. It attained the smallest error rate compared to all machine learning and deep learning models, as demonstrated in Table 3. The estimated load profile of a commercial buildings is illustrated in Figure 8.  
To evaluate our model properly, we benchmarked our results against similar studies that used public data sets and are commonly applied for load forecasting in the literature for Smart Grid, Smart City [40] in Table 4. In this study, we implemented our proposed method to the same data set used in [41] and selected 69 customer's consumption values between 1 June 2013 to 31 August 2013 time period. As can be seen in Table 4, our proposed model outperformed similar studies and we obtained an average MAPE value, 31.481, in the same testing interval.