## 5 Experimentation and Analysis
### Analysis of Experimental Arithmetic
#### 5.4.4 Comparison of Four-Season Load Forecasting Models with Weather  
The experiment is to implement load forecasting under conditions with weather characteristics. Temperature, humidity, atmospheric pressure and load values are used as inputs. Among them, the performance metrics of the improved model proposed in this study have been bolded in bold. (a) The Figure 7 experiment without weather for one is a multiple-input, single-output experiment. In this case, meteorological data features such as the temperature and humidity are used, which are input into the model as additional feature variables, and feature selection or dimensionality reduction techniques are used to filter out features that have a greater impact on power load forecasting, thus reducing the impact of weather disturbances on the forecasting results. The table also compares the metrics of the MVMO-LSTM with each algorithm from top to bottom. The metrics for each of the four seasons are compared from left to right. The \"target\" is the raw data. (b) From Figure 7 and Table 3, it can be seen that when the input conditions increase, the accuracy of the load prediction is affected so as to be worse compared to the load pre-diction without weather features. However, the four-season average of the RMSE, MAE, and R\({}^{2}\) of the MVMO-LSTM can still be maintained at 0.3250 KW, 0.2828 KW, and 98.85%. The average RMSE, MAE and R\({}^{2}\) compared to the MVMO-LSTM without weather are 0.2530 KW, 0.2195 KW and 99.27%, respectively. It can be concluded that the influential factor of weather has an influential relationship on short-term power load forecasting. However, the hybrid model proposed in this paper is still superior in all the indicators when compared with the model combined with ordinary intelligent algorithms. The RMSE and MAE of each season are lower, and the R\({}^{2}\) is closer to 1. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Spring**} & \multicolumn{3}{c}{**Summer**} & \multicolumn{3}{c}{**Autumnn**} & \multicolumn{3}{c}{**Winter**} \\ \cline{2-13}  & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline MVMO-LSTM & **0.2448** & **0.1899** & **0.991** & **0.2596** & **0.2238** & **0.996** & **0.2935** & **0.2832** & **0.991** & **0.2140** & **0.1810** & **0.993** \\ \hline LSTM & 1.0860 & 0.8909 & 0.806 & 1.2886 & 1.0724 & 0.883 & 0.5294 & 0.4682 & 0.789 & 1.4795 & 1.3237 & 0.911 \\ \hline FOA-LSTM & 0.7702 & 0.6614 & 0.847 & 1.3927 & 1.2455 & 0.895 & 0.5212 & 0.4222 & 0.797 & 1.5234 & 1.3238 & 0.910 \\ \hline GWO-LSTM & 0.2696 & 0.2087 & 0.985 & 0.5308 & 0.4506 & 0.983 & 0.3013 & 0.2755 & 0.986 & 0.3688 & 0.3367 & 0.977 \\ \hline SSA-LSTM & 0.2776 & 0.2159 & 0.981 & 0.5172 & 0.4299 & 0.986 & 0.2467 & 0.2156 & 0.988 & 0.5416 & 0.4342 & 0.971 \\ \hline DBO-LSTM & 0.2819 & 0.2183 & 0.980 & 0.7232 & 0.4382 & 0.937 & 0.3126 & 0.2978 & 0.984 & 0.3545 & 0.3157 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the performance comparison of the four-season combination prediction model without weather. (c) Figure 8 shows a plot of the four-season prediction model error comparisons, which is shown on the left half of the figure for the four-season model error comparisons without input features, such as weather, and on the right half of the figure for the four-season model error comparisons with input features, such as weather. The closer the error is to 0, the better the prediction of the model. It can be concluded from this figure that whether the MVMO-LSTM is applied to load data without input features such as weather or with input features such as weather, the error of the model is closer to 0 compared to the other algorithms.