# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 4 Case Studies and Discussion
### Comparison and Analysis of Medium-Term Electric Power Load Forecasting
In addition, \(\Delta\) is the difference between \(\Delta_{DNN}\) and \(\Delta_{LSTM}\). The MAE, RMSE, CVRMSE (%), MAPE, R\({}^{2}\), and calculation time of \(\Delta_{DNN}\)'s were better than those of \(\Delta_{LSTM}\) by \(-\)2.22 MW, \(-\)4.06 MW, \(-\)6.56%, \(-\)4.63%, 0.01, and \(-\)5.67 ms, respectively. In conclusion, on average, the DNN showed a better performance in terms of prediction error and calculation time compared to the LSTM, regardless of the power consumption pattern. The DNN, of the company with a regular power consumption pattern exhibited the best performance. Figure 11 shows the error between the actual and predicted values for the proposed DNN and LSTM for company B in April 2019. April in Korea is the representative month of spring. Figure 11a shows the error between the actual and predicted values using the proposed DNN. Figure 11b depicts the error between the actual and predicted values using the proposed LSTM. The error range in Figure 11a is within \(-\)8 to 8, while that in Figure 11b is \(-\)10 to 70. The proposed DNN method was superior to the proposed LSTM (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Month**} & \multirow{3}{*}{**Metrics**} & \multicolumn{3}{c}{**Company B**} & \multicolumn{3}{c}{**Company T**} & \multicolumn{3}{c}{\(\Delta\)} \\ \cline{3-10}  & & **DNN** & **LSTM** & \(\Delta_{\text{B}}\) & **DNN** & **LSTM** & \(\Delta_{\text{T}}\) & \(\Delta_{\text{DNN}}\) & \(\Delta_{LSTM}\) \\  & & **(A)** & **(B)** & **(A-B)** & **(C)** & **(D)** & **(C-D)** & **(A-C)** & **(B-D)** \\ \hline \multirow{6}{*}{December} & MAE(MW) & 0.09 & 0.22 & \(-\)0.13 & 0.16 & 1.29 & \(-\)1.13 & \(-\)1.07 & \(-\)0.07 \\  & RMSE(MW) & 0.14 & 0.45 & \(-\)0.31 & 0.35 & 2.00 & \(-\)1.65 & \(-\)1.55 & \(-\)0.21 \\  & CVRMSE(\%) & 0.52 & 1.68 & \(-\)1.16 & 0.31 & 1.80 & \(-\)1.49 & \(-\)0.12 & 0.21 \\  & MAPE (\%) & 0.44 & 0.84 & \(-\)0.40 & 0.17 & 1.36 & \(-\)1.19 & \(-\)0.52 & 0.27 \\  & R\({}^{2}\) & 0.99 & 0.99 & 0 & 0.99 & 0.99 & 0 & 0 & 0 \\  & Time (ms) & 1.39 & 4.16 & \(-\)2.77 & 1.76 & 4.67 & \(-\)2.91 & \(-\)0.51 & \(-\)0.37 \\ \hline \multirow{6}{*}{Average} & MAE(MW) & 0.15 & 0.70 & \(-\)0.55 & 0.25 & 1.92 & \(-\)1.67 & \(-\)0.10 & \(-\)2.62 \\  & RMSE(MW) & 0.34 & 1.90 & \(-\)1.56 & 0.41 & 2.91 & \(-\)2.50 & \(-\)0.07 & \(-\)4.81 \\ \cline{1-1}  & CVRMSE(\%) & 1.20 & 6.52 & \(-\)5.32 & 0.60 & 1.84 & \(-\)1.24 & 0.60 & \(-\)8.36 \\ \cline{1-1}  & MAPE (\%) & 1.42 & 4.76 & \(-\)3.34 & 0.18 & 1.47 & \(-\)1.29 & 1.24 & \(-\)6.23 \\ \cline{1-1}  & R\({}^{2}\) & 0.99 & 0.99 & 0 & 0.99 & 0.99 & 0 & 0 & 0 \\ \cline{1-1}  & Time (ms) & 1.37 & 4.12 & \(-\)2.75 & 1.66 & 4.58 & \(-\)2.92 & \(-\)0.29 & \(-\)8.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)Figure 12 shows the error between the actual and predicted values of the DNN and the LSTM proposed for company T in May 2019. In Korea, May is also the representative month of spring. Figure 12a shows the error between the actual and predicted values using the proposed DNN. Figure 12b depicts the error between the actual and predicted values using the proposed LSTM. The error range in Figure 12a is \(-2\) to \(2\), while that in Figure 12b is \(-15\) to \(35\). The proposed DNN method had an excellent performance because the error range was much smaller than that of the proposed LSTM. Figure 11: Error between the actual and predicted values using the proposed DNN and LSTM (company B). Figure 12: Error between the actual and predicted values using the proposed DNN and LSTM (company T).