Electric Load Forecasting Based on Deep Ensemble Learning

Abstract

Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enterprise production cycles exert uncontrollable effects on the electric grid load. While the causal convolutional neural network can significantly enhance long-term sequence prediction, it may suffer from problems such as vanishing gradients and overfitting due to extended time series. To address this issue, this paper introduces a new power load data anomaly detection method, which leverages a convolutional neural network (CNN) to extract temporal and spatial information from the load data. The features extracted are then processed using a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies in the data more adeptly. An enhanced random forest (RF) classifier is employed for anomaly detection in electric load data. Furthermore, the paper proposes a new model framework for electricity load forecasting that combines a dilated causal convolutional neural network with ensemble learning. This combination addresses issues such as vanishing gradients encountered in causal convolutional neural networks with long time series. Extreme gradient boosting (XGBoost), category boosting (CATBoost), and light gradient boosting machine (LightGBM) models act as the base learners for ensemble modeling to comprehend deep cross-features, and the prediction results generated by ensemble learning serve as a new feature set for secondary ensemble modeling. The dilated convolutional neural network broadens the receptive field of the convolutional kernel. All acquired feature values are concatenated and input into the dilated causal convolutional neural network for training, achieving short-to-medium-term electric load forecasting. Experimental results indicate that compared to existing models, its root mean squared error (RMSE) and mean squared error (MSE) in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%, respectively, underscoring the efficacy of the proposed framework.