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

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 ens...