# 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  
Table 4 shows the comparison of the proposed DNN and LSTM for medium-term electric power load forecasting. Lower values of MAE (MW), RMSE (MW), CVRMSE (%), and MAPE (%) denote higher model accuracy. The R2 values approaching one and the score values of approximately 100% show that the predictive value approximates the actual value. To compare the performances of the proposed DNN and LSTM, the difference between the DNN and LSTM of company B with similar power consumption patterns is expressed by \(\Delta_{B}\), and the difference between the DNN and LSTM of company T having irregular power consumption patterns is expressed by \(\Delta_{T}\). Meanwhile, to compare the performances of companies B and T with irregular power consumption patterns, the differences between the proposed DNN and LSTM are represented by \(\Delta_{DNN}\) and \(\Delta_{LSTM}\), respectively. \begin{table}
\begin{tabular}{c 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** & \(\Delta_{T}\) & \(\Delta_{DNN}\) & \(\Delta_{LSTM}\) \\  & & **(A)** & **(B)** & **(A–B)** & **(C)** & **(D)** & **(C–D)** & **(A–C)** & **(B–D)** \\ \hline \multirow{6}{*}{January} & MAE(MW) & 0.15 & 0.43 & \(-\)0.28 & 0.19 & 1.09 & \(-\)0.90 & \(-\)0.66 & \(-\)0.04 \\  & RMSE(MW) & 0.30 & 0.97 & \(-\)0.67 & 0.26 & 1.75 & \(-\)1.49 & \(-\)0.78 & 0.04 \\  & CVRMSE(\%) & 0.73 & 2.37 & \(-\)1.64 & 0.21 & 1.44 & \(-\)1.23 & 0.93 & 0.52 \\  & MAPE (\%) & 0.58 & 0.94 & \(-\)0.36 & 0.16 & 0.88 & \(-\)0.72 & 0.06 & 0.42 \\  & R2 & 0.99 & 0.99 & 0 & 0.99 & 0 & 0 & 0 & 0 \\  & Time (ms) & 1.37 & 4.17 & \(-\)2.80 & 1.64 & 4.28 & \(-\)2.64 & \(-\)0.11 & \(-\)0.27 \\ \hline \multirow{6}{*}{February} & MAE(MW) & 0.15 & 0.78 & \(-\)0.63 & 0.13 & 1.02 & \(-\)0.89 & \(-\)0.24 & 0.02 \\  & RMSE(MW) & 0.35 & 2.37 & \(-\)2.02 & 0.28 & 1.67 & \(-\)1.39 & 0.70 & 0.07 \\ \cline{1-1}  & CVRMSE(\%) & 0.81 & 5.49 & \(-\)4.68 & 0.23 & 1.37 & \(-\)1.14 & 4.12 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the DNN and LSTM performances for medium-term electric power load forecasting. In the medium-term power prediction, the MAE, RMSE, CVRMSE (%), and MAPE of company B with a constant power consumption pattern indicated better performance than the proposed LSTM, with an average DNN of 0.55 MW, 1.56 MW, 5.32%, and 3.34%, respectively. The calculation time for company B was better than the proposed LSTM by an average of 2.75 ms. The R\({}^{2}\) for company B was approached 0.99 for the proposed DNN and LSTM, except for the proposed LSTM for April (R\({}^{2}\) = 0.97). The MAE, RMSE, CVRMSE (%), and MAPE of company T with irregular power consumption patterns outperformed the proposed LSTM with an average of 1.67 MW, 2.5 MW, 1.24% and 1.29%, respectively. The calculation time of company T was better than the proposed LSTM by 2.92 ms on average. The R\({}^{2}\) for company T approached 0.99 for the proposed DNN and LSTM, except for the LSTM of the months of May (R\({}^{2}\) = 0.88) and September (R\({}^{2}\) = 0.95). The comparison results of MAE, RMSE, CVRMSE (%), MAPE, R\({}^{2}\), and calculation time for the proposed DNN and LSTM showed that the DNN and LSTM of company B with a better power consumption pattern outperformed those of company T with an irregular power consumption pattern. The comparison of MAE, RMSE, CVRMSE (%), MAPE, R\({}^{2}\), and calculation time of the proposed DNN illustrated that company B outperformed company T by 0.1 MW, 0.07 MW, 0.6%, 1.24%, 0, and 0.29 ms, respectively. Meanwhile, the comparison results of MAE, RMSE, CVRMSE (%), MAPE, R\({}^{2}\), and calculation time for the proposed LSTM showed that company B outperformed company T by 2.62 MW, 4.81 MW, 8.36%, 6.23%, 1.97, and 8.7 ms, respectively.