# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 4 Case Analysis
### Comparative Analysis and Conclusions
This is because the indicators selected in the cases used in this paper were selected according to experience, so mRMR results in a slight decrease in accuracy. However, mRMR could greatly reduce the workload of data processing in the process of actual load forecasting with large index system and abundant data in the multi-energy coupling scenario, and at the same time, it will not have a great impact on the forecasting accuracy. Therefore, mRMR-AFWA-LSSVM is still the optimal load forecasting model as a whole. Finally, we could summarize the following model test results: \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **Time(s)** & **MSE** & **RMSE** & **MAE** & **MAPE\%** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Multi-energy \\ coupling \\ \end{tabular} } & mRMR-AFWA-LSSVM & 111.3100 & 2.050467 & 1.431945 & 1.103184 & 2.080982 & 0.996127 \\  & AFWA-LSSVM & 145.9760 & 0.661562 & 0.813365 & 0.711322 & 2.013296 & 0.997686 \\  & LSSVM & 10.5528 & 31.99167 & 5.656118 & 4.30042 & 7.468827 & 0.939572 \\  & AFWA-SVM & 10.6015 & 12.61361 & 5.351565 & 3.418807 & 7.076095 & 0.976175 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Total \\ demand \\ \end{tabular} } & mRMR-AFWA-LSSVM & 140.9960 & 45456.266 & 67.41859 & 52.3335 & 4.56392 & 0.956144 \\  & AFWA-LSSVM & 151.3300 & 4455.127 & 66.74674 & 51.79472 & 4.518415 & 0.957014 \\  & LSSVM & 8.9416 & 5877.982 & 76.668 & 70.22673 & 6.752522 & 0.943286 \\  & AFWA-SVM & 17.5940 & 7298.541 & 85.4315 & 71.40998 & 6.138677 & 0.929579 \\ \hline \end{tabular}
\end{table} 
Table 9: Model comparison and evaluation. * In terms of forecasting accuracy, AFWA effectively improved the forecasting accuracy of the LSSVM model, increasing the multi-energy coupling supply forecast by about 5% and the total load forecast by about 2%. In fact, the application of LSSVM significantly improved the prediction accuracy in our experiment compared with the SVM model, which the multi-energy coupling supply forecast increased by about 6%, and the total load forecast increased by about 4%. Compared with the PSO-LSSVM model, the AFWA-LSSVM model had a certain degree of efficiency improvement, with the multi-energy coupling supply forecast increasing by about 0.4% and the total load forecast increasing by about 0.3%. * In terms of computing efficiency, the application of mRMR significantly sped up the computing speed of the model, and could assist scene analysis to a certain extent. Those with structural connections or similar key nodes tended to have a large degree of redundancy, while those with weak correlation had a small degree of correlation. * At the same time, aiming at the optimization problem of future application of the model, we could start from digging into the key influencing factors of demand prediction. The accuracy of multi-energy coupling forecasting was better than that of total load forecasting, its better indicator system was partly to blame. * It could be found from the analysis that the total load demand was the most important factor affecting the power network load supply, and also the main source of errors. The appropriate influence factor selection and feature analysis would improve the accuracy of forecasting mode.