## 6 Case Analysis and Result
### Comparison of the Predicted Results  
To validate the effectiveness of the proposed method in this paper, experiments were conducted using a Bi-LSTM network to predict three types of loads on different test days. An LSTM network was selected as the baseline model for comparison. After dividing the load types, for each type of load, the data were divided into training, testing, and validation sets in a ratio of 6:2:2.  
When training neural networks, overfitting is prone to occur. We used the Mean Absolute Percentage Error (MAPE) as the basis for judging the overfitting phenomenon and used the best feature matrix obtained as input. The training cycle and the number of network layers of the LSTM and Bi-LSTM network were determined.  
As the main focus of this paper is load-type analysis and the correction of input features, it was essential to ensure consistent parameters for the LSTM and BI-LSTM neural networks used for comparison. Regarding the selection of the hyperparameters, we followed the approach outlined in reference [43]. We conducted multiple experiments using the method of controlled variables for optimization. The experimental results are presented in Table 5.  
Starting with a single-layer network and 100 training iterations as a baseline, we gradually increased the number of training iterations. It can be observed that the MAPE initially decreased and then increased, indicating that 250 iterations provided the optimal training duration. Keeping the training iterations fixed at 250, we progressively increased the number of neural network layers. The MAPE reached its minimum value when the network had three layers.  
In conclusion, the parameters for the BI-LSTM and LSTM neural networks were ultimately selected as follows: three layers for the network depth and 250 training iterations.  
The LSTM and Bi-LSTM models that utilized historical loads as inputs are referred to as Model 1 and Model 2, respectively. The LSTM and Bi-LSTM models that used reconstructed features as inputs based on the proposed method are referred to as Model 3 and Model 4, respectively. Additionally, to evaluate the improvement of the proposed method, the following traditional models were selected for comparison: LSTM neural network with simple feature selection using Pearson correlation coefficient [44] (PCC-LSTM) and the Bi-LSTM neural network with simple feature selection using Pearson correlation coefficient (PCC-BI-LSTM). The prediction results and corresponding indicators are shown in the table below.  
From Tables 6 and 7, it can be observed that for the three different types of load profiles, the mid-temperature day exhibited smaller load fluctuations and higher similarity between different dates. The accuracy of the load forecasting using various models was higher in this case. However, the combined impact of the cooling load and the DPVS output on high-temperature days was difficult to measure, resulting in lower prediction accuracy. Moreover, compared to the LSTM model, the BI-LSTM model performed better due to its ability to consider bidirectional information when provided with the same input. After applying the input feature reconstruction method proposed in this paper, the prediction accuracy of both the LSTM and BI-LSTM models improved for all three types of load  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training** & **Network Layer** & **MAPE** & **Training** & **Network Layer** & **MAPE** \\
**Cycle/Round** & **Number** & & **Cycle/Round** & **Number** & **Number** & \\ \hline
100 & 1 & 4.71 & 250 & 1 & 2.98 \\
150 & 1 & 3.95 & 250 & 2 & 2.88 \\
200 & 1 & 3.44 & 250 & 3 & 2.35 \\
250 & 1 & 2.98 & 250 & 4 & 2.55 \\
300 & 1 & 3.14 & 250 & 5 & 4.53 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model test result.