Short-term Load Forecasting Based on CEEMDAN-LSTM-AdaBoost

• Published in: Journal of physics. Conference series Vol. 2731; no. 1; pp. 12038 - 12044
• Main Authors: Xi, Lingling, Gong, Dandan
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2024
• Subjects: Accuracy; AdaBoost; Adaptive algorithms; CEEMDAN; Energy utilization; Forecasting; load forecasting; LSTM; Machine learning; Mathematical models; Neural networks; Prediction models; System effectiveness
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2731/1/012038

High precision load forecasting is of great significance for effectively allocating resources in energy systems and improving energy utilization efficiency. In order to improve the accuracy of load forecasting, this paper proposes a new load forecasting model based on the adaptive noise complete ensemble empirical mode decomposition (CEEMDAN)-long short-term memory neural network (LSTM)-adaptive boosting algorithm (AdaBoost). Firstly, CEEMDAN is used to decompose the original load sequence to obtain a series of eigenmode components, which can reduce the impact caused by the non-stationary nature of data. Then, these eigenmode components are input into LSTM for prediction, and an integrated learning model by AdaBoost is introduced. A strong predictor is constructed through several weak predictors to increase the prediction accuracy. Finally, the prediction result of each component is superimposed and reconstructed to obtain the final prediction result. Compared with other models, the two evaluation indicators of the proposed prediction model have decreased by 35.52% and 76.61% respectively, indicating the good prediction accuracy and generalization performance of the proposed method.