# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### DCC-EL Forecasting Framework
#### 2.3.3 DCC-EL Ensemble Model  
In the context of power load forecasting, as illustrated in Figure 4, a singular module is deployed for feature extraction. This study employs a one-dimensional DCC to scan and identify relevant time series variation features, such as wind speed and temperature, thereby automatically extracting associated time series information. Subsequently, an ensemble model is incorporated as one of the sub-modules, and the power load label is presented as a single-target regression task. This approach, which emphasizes deep feature extraction more than multi-target regression tasks, is adopted because of the single-target model's precision and loss function. Consequently, the ensemble model's ability to learn cross-features from data to target results in rich feature information after profound mining [34]. By leveraging these pre-trained cross-features in conjunction with the attributes of DCC, the learning threshold of the DCC's standalone model within the combined model can be diminished. This approach accentuates the feature information that the power load prediction model might overlook, rendering it more apt for the forecasting task in this specific context. In the construction of the DCC-EL model, an ensemble learning strategy was initially adopted, with top-tier base learners such as XGBoost, CATBoost, and LightGBM integrated. The advantage of this ensemble approach was found in its capacity to analyze data from multiple dimensions, with prediction accuracy significantly enhanced and robust resilience imparted to the model. Through these base learners, intersecting features from the data were delved into and extracted, laying a solid foundation for modeling complex nonlinear relationships. Subsequently, to capture the temporal dependencies of time series data precisely, the dilated causal convolution neural network was incorporated. The essence of this network is its unique dilated convolution design, allowing the characteristics of input data to be perceived across a broader time window, recognized as crucial for extracting long-term dependency patterns in the data. After deep feature mining through ensemble learning and temporal modeling with the dilated causal convolution network, high-precision forecasts of power load were achieved  
Figure 4: Details of dilated causal convolutional neural network. by the DCC-EL model. It is worth noting that during the prediction process, the importance of each feature was integrated to allocate weights, ensuring a heightened focus on those characteristics seen as having a pivotal impact on the outcome. This strategy not only further bolstered prediction accuracy but also enhanced the interpretability of the model, aiding in a better understanding of the causal relationships underpinning the forecasted results. The specific procedure is outlined as follows:  
(1) Ensemble learning for crossover feature extraction: The input layer feeds data into the ensemble learning model for power load prediction. Each sample is predicted via crossover prediction to determine the leaf node position of each sample within the ensemble learning model. For instance, if the model comprises three subtrees, and the number of leaf nodes for Tree1, Tree2, and Tree3 is 3, 3, and 4, respectively, and if sample data fall onto the 1 st, 2 nd, and 3 rd leaf nodes, respectively, the resulting crossover feature for the sample data would be represented as the vector [1,0,0,0,1,0,0,0,1,0]. (2) Feature engineering: Extensive feature processing, primarily for weather and time, is undertaken in this step. For example, wind speed, which has a distinct ordinal quality, is encoded sequentially. Temporal characteristics are extracted, encompassing essential elements such as year, month, and day; seasonal distinctions like spring, summer, autumn, and winter; and details like holiday factors, weekend status, the specific day of a holiday, days leading up to a holiday, and days following a holiday.