# Electric Load Forecasting Based on Deep Ensemble Learning
## 1 Introduction
### Related Works
#### 1.1.2 Intelligent Forecasting Methods  
Intelligent forecasting models encompass machine learning and its subfield, deep learning. Machine learning is a field of artificial intelligence, with its central idea being to allow computers to learn patterns and regularities from data, enabling tasks such as prediction, classification, and clustering. Through machine learning, computers can automatically uncover valuable information from vast amounts of data, thereby assisting in decision making, enhancing efficiency, and solving complex problems. Deep learning is a subset of machine learning and represents a significant branch. This contemporary technique builds upon traditional neural networks, offering enhanced classification and prediction accuracy. Intelligent methods are widely acknowledged in academia and industry [7] and have applications in the electrical fields. For example, [PERSON] et al. [8] established a prediction model based on the genetic algorithm-backpropagation (GA-BP) neural network. They formed a continuous linear time series for training by incorporating a sliding window and other techniques. This approach mitigated the overfitting problem often seen with more traditional methods and demonstrated that the proposed framework achieved higher forecasting accuracy than conventional methods. [PERSON] et al. [9] designed a short-term load forecasting method for power systems based on gradient boosting trees. This method preprocesses data by quantifying short-term load-influencing factors using fuzzy probabilities, applies difference decomposition to handle short-term load data, and establishes a robust regression gradient boosting tree in the direction of the negative gradient of the loss function to obtain the load forecasting results. [PERSON] et al. [10] proposed a hybrid approach utilizing both the Prophet and long short-term memory (LSTM) models. Initially, the Prophet model forecasts the raw load data by leveraging both linear and nonlinear data. However, some nonlinear data remain, which are then trained using the LSTM. Ultimately, the predictive outputs from both the Prophet and LSTM are trained through a backpropagation neural network (BPNN) to further enhance the prediction accuracy. [PERSON] et al. [11] proposed an attention-based convolutional neural network (CNN) model that combines both LSTM and bidirectional long short-term memory (BiLSTM). By utilizing CNN and the attention mechanism, we extract key factors influencing the load. Subsequently, the LSTM and BiLSTM are employed to forecast future electricity load data. [PERSON] et al. [12] proposed a set of machine learning (ML) models to enhance the accuracy of electric load forecasting, including multiple linear regression (MLR), k-nearest neighbor regressor (KNN), epsilon support vector regression (SVR), the random forest regressor (RF), and the extreme gradient boosting regressor (XGB). Experiments show that the model constructed using XGB outperforms those built with other algorithms. [PERSON] et al. [13] employed an attention-based LSTM network model for electric load forecasting. Firstly, they constructed a feature-based attention encoder to compute the correlation between input features at each time step and the electric load. Secondly, they developed a time-based attention decoder to delve into temporal dependencies. Subsequently, the LSTM model integrated these attention outcomes. Finally, the pinball loss function was used to obtain probabilistic forecasts. [PERSON] et al. [14] proposed a machine learning model that utilizes gated recurrent units (GRU) and random forest (RF). The GRU is employed for predicting power load, while the RF is used to reduce the input dimensionality of the model. This is the first time that GRU and RF have been jointly applied for short-term load forecasting. [PERSON] et al. [15] introduced a predictive model based on the LSTM neural network and the light gradient boosting machine (LightGBM) integrated with variational mode decomposition (VMD).