# Electric Load Forecasting Based on Deep Ensemble Learning
## 3 Results  
The proposed CNN-BiLSTM-RF model and DCC-EL framework experiments are trained and tested on Centos 7 operating system with Intel(R) Xeon(R) Gold 6154 CPU, 128G RAM, NVIDIA TITAN V GPU and 24 G video memory, and the programming language is Python. We first conducted anomaly detection on the data to prioritize essential features during the initial training. Using the CNN-BiLSTM-RF model, we detected anomalies across four major industries: commercial, large industrial, non-general industrial, and general industrial. The parameter settings for the CNN-BiLSTM-RF model are detailed in Table 1. We then applied the 3 Sigma rule, identifying data points that deviated by more than three times the historical data standard deviation as abnormal. Finally, we pinpointed change points by intersecting results from the two methods, as illustrated in Figure 5. This visualization depicts the anomalies detected across the four major industries, further detailed in Figure 6. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Hidden layers in CNN & 3 \\ Filters & 64 \\ Channels & 3 \\ Active function & ReLU \\ Units in LSTM & 100 \\ Learning-rate & 0.001 \\ Dropout & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CNN-BiLSTM-RF neural network model parameter settings. Figure 5: Visualization of the four major industries’ anomalies detected. Figure 6: Visualization of the four major industries’ anomalies detected by 3 Sigma rule. The change points represent the difference between the current change points and the load from the previous moment. These change points and their amplitudes are indicated in the series of daily maximum loads, as depicted in Figure 5, where the red points denote the identified change points. Based on the data in Figure 6, one can observe that within the series of daily maximum loads, the business sector exhibits the largest change amplitude at 4 \(\times\) 10\({}^{6}\). In contrast, the industrial non-ordinary industry has the smallest amplitude, registering only at 2 \(\times\) 10\({}^{3}\). We use weather changes as the explanatory variable, denoting 'unchanged' as 0 and 'changed' as 1. Logistic regression (LR) is a regression method that employs maximum likelihood estimation to address problems wherein the explanatory variable is categorical:  
\[y=\frac{1}{1+e^{-(\omega^{T}x+b)}} \tag{37}\]  
Table 2 displays logistic regression results for the change-driving factors of key indicators across four primary industrial categories: business, big industry, non-general industry, and general industry. The first row represents the regression coefficient for each feature indicator, while the second row shows the standard error value. From the logistic regression results presented in Table 2 for the panel data, it is evident that the factors influencing sudden load changes differ across industries. Start_weather has the most significant influence in the business sector, whereas in the big industry sector, end_weather plays a more crucial role. The minimum temperature predominantly influences both the non-general and general industries. Upon ranking the features based on their significance, those of greater importance are assigned higher weights, while those of lesser significance receive lower weights. These features and their designated weights are subsequently inputted into the DCC-EL model to predict future power load values. The DCC-EL model incorporates these feature weights during its prediction process. Table 3 details the parameter settings for the DCC-EL model.