Ultra short-term power load forecasting based on similar day clustering and ensemble empirical mode decomposition

HIGHLIGHTS

• who: Wenhui Zeng and colleagues from the School of Electronics and Information Engineering, Tongji University, Shanghai, China have published the research work: Ultra Short-Term Power Load Forecasting Based on Similar Day Clustering and Ensemble Empirical Mode Decomposition, in the Journal: Energies 2023, 16, 1989. of 17/03/2022
• what: The authors propose an ultra short-term power load method and EEMD (Ensemble
• how: In this paper the K-means clustering algorithm was used to cluster the historical data. The final LSTNet forecast result was obtained by superimposing the results of the nonlinear . . .
  

   

  

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