Short-term load forecasting method based on secondary decomposition and improved hierarchical clustering

• Published in: Results in engineering 2024-06, Vol.22, p.101993, Article 101993
• Main Authors: Zha, Wenting, Ji, Yongqiang, Liang, Chen
• Format: Article
• Language: English
• Subjects: CEEMDAN; Hierarchical clustering; Sample entropy; Short-term load forecasting; STL
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2024.101993

In the context of large-scale grid connection of new energy, short-term load forecasting is a vital and challenging task for power system to balance supply and demand. To effectively improve the forecasting accuracy, a new load forecasting method is proposed aiming to mine the characteristics of load data and study the application of artificial intelligence algorithms. In this paper, the seasonal and trend decomposition using loess (STL) method is firstly applied to decompose the load data into the trend, seasonal and residual components and the residual component with the highest complexity is further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) approach. Secondly, in order to reduce the number of components, the improved hierarchical clustering technique is proposed to cluster all intrinsic mode functions (IMFs) obtained by CEEMDAN into high-frequency and low-frequency components. Then, different network models are trained to get the prediction results for different components, and the total load prediction value is achieved by stacking all of them. Finally, the national demand dataset of Great Britain in 2021–2022 is used to conduct the ablation and comparative experiments. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method are 2.064% and 724.01 MW, respectively, which verified the effectiveness and advancement of the proposed method. •Features of load data are effectively extracted by secondary decomposition method.•The improved clustering method is proposed to reduce the number of IMFs.•A combined forecasting model is built based on the features of different components.•Ablation and comparative experiments are given to prove the superiority of the model.