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Summary of Research on Short-term Load Forecasting Model Based on Vmd and Ipso-elm, by Qiang Xie


Research on short-term load forecasting model based on VMD and IPSO-ELM

by Qiang Xie

First submitted to arxiv on: 4 Oct 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Neural and Evolutionary Computing (cs.NE)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This study introduces an advanced combined forecasting method that integrates Variational Mode Decomposition (VMD) with an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the Extreme Learning Machine (ELM). The VMD algorithm decomposes original power load data into high-frequency and low-frequency sequences, which are then categorized based on mutual information entropy theory. The IPSO-ELM prediction model independently predicts these sequences and reconstructs the data to achieve final forecasting results. Simulation results show that this method improves prediction accuracy and convergence speed compared to traditional ELM, PSO-ELM, and PSO-ELM methods.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper develops a new way to forecast power load in wind farms. It uses special techniques like VMD and IPSO to improve the accuracy of predictions. The study breaks down the original data into different parts based on how often they happen. Then it predicts these parts separately and combines them to get the final result. This method does better than other methods at predicting the power load and takes less time to do it.

Keywords

* Artificial intelligence  * Optimization