# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 5 Experimental Results and Analysis
### Comparing Metric  
In this section, we compare the prediction results of different models, and box and whisker is used to show the comparisons of different models. We only show the box and whisker in 2019 to observe the comparison of three metrics, which is similar to the comparison of the years 2020 and 2021.  
Figures 9-11 use box and whisker to show the comparison of three metrics on three groups of models, respectively, for one-step, three-step, five-step, and seven-step electricity load forecasting in 2019. We can see that these three metrics rise with the increase of the future step, which means that all those models perform worse with the increase of the future step. Meanwhile, models with NODE conspicuously have advantages compared with models without NODE for three-step, five-step, and seven-step electricity load forecasting. Tables 2-4 show the comparison of 2019, 2020, and 2021. In Table 2, we can see that the average metrics of NODE models distinctly outperform models without NODE. For example, for seven-step forecasting in 2019, models with NODE outperform models without NODE in terms of RMSE, by 13.95%, 9.87%, and 1.18%, respectively. For seven-step forecasting in 2020, the improved rates of RMSE are 22.69%, 12.62%, and 12.56%. For seven-step forecasting in 2021, the RMSE reduces by 3.76%, 18.16%, and 23.58% respectively. As we can observe from Figures 9-11 and Tables 2-4, generally, we can see that the LSTM and BiLSTM model with NODE is better than models without NODE. However, models with NODE seem to perform similar to models without NODE when step is 1. The reasons are discussed in Section 5.3.  
Figure 11: The MAE metric comparison of three groups of models, respectively, in future steps 1, 3, 5, and 7 in Queensland in 2019. The dotted line divides the different comparative model groups. In each comparative model group, different colors represent different steps. The model with NODE is on the left side and the model without NODE is on the right side.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**RMSE (MW)**} & \multicolumn{4}{c}{**MAE (MW)**} & \multicolumn{4}{c}{**MAPE (\%)**} \\ \cline{2-13}  & **1** & **3** & **5** & **7** & **1** & **3** & **5** & **7** & **1** & **3** & **5** & **7** \\ \hline \hline
* & 80.79 & **115.87*
* & 170.25 & **205.22*
* & 1.30\% & **1.88\%*
* & 2.82\% & **3.47\%*
* \\ CBL & 109.52 & 173.97 & 263.20 & 321.54 & 84.85 & 127.59 & 195.62 & 231.84 & 1.36\% & 2.08\% & 3.23\% & 3.83\% \\ C\textbackslash{}DBL & 104.35 & 156.59 & 241.33 & 285.82 & 87.39 & 119.51 & 184.16 & 215.02 & 1.45\% & 1.96\% & 3.09\% & 3.63\% \\ BBL & 106.78 & 197.39 & **272.31*
* & 317.13 & 87.26 & 148.85 & 202.27 & 232.57 & 1.44\% & 2.45\% & 3.35\% & 3.85\% \\ \textbackslash{}OBBL & 145.98 & **152.18*
* & **216.27*
* & 316.55 & 124.51 & 118.83 & **161.14*
* & 239.11 & 2.03\% & 1.96\% & **2.66\%*
* & 4.01\% \\ BBLL & **92.73*
* & 206.86 & 278.42 & 319.78 & **73.03*
* & 144.16 & 197.28 & 231.89 & **1.20\%*
* & 2.32\% & 3.20\% & 3.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results: average RMSE, MAE, and MAPE in 2019.
* indicates the model contains NODE block.  
Figure 10: The MAPE metric comparison of three groups of models, respectively, in future steps 1, 3, 5, and 7 in Queensland in 2019. The dotted line divides the different comparative model groups. In each comparative model group, different colors represent different steps. The model with NODE is on the left side and the model without NODE is on the right side.