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 |
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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