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

TL;DR

This paper presents a novel short-term power load forecasting model that combines Variational Mode Decomposition with an improved optimization algorithm to enhance accuracy and convergence speed.

Contribution

It introduces an IPSO-ELM model optimized with chaos mapping and elite learning, improving load forecasting performance over traditional methods.

Findings

Significantly improved prediction accuracy

Faster convergence compared to traditional models

Effective decomposition of load data into frequency components

Abstract

To enhance the accuracy of power load forecasting in wind farms, 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). Initially, the VMD algorithm is employed to perform high-precision modal decomposition of the original power load data, which is then categorized into high-frequency and low-frequency sequences based on mutual information entropy theory. Subsequently, this research profoundly modifies the traditional multiverse optimizer by incorporating Tent chaos mapping, exponential travel distance rate, and an elite reverse learning mechanism, developing the IPSO-ELM prediction model. This model independently predicts the high and low-frequency sequences and reconstructs the data to achieve the final forecasting results. Simulation results indicate that the proposed method significantly improves prediction accuracy and convergence speed compared to traditional ELM, PSO-ELM, and PSO-ELM methods.