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

arXiv: https://arxiv.org/abs/2410.05300 · HTML: https://mlsanity.com/paper/2410.05300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach](https://mlsanity.com/paper/2107.07772/) (2021)
- [Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties](https://mlsanity.com/paper/1808.05691/) (2018)
- [Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data](https://mlsanity.com/paper/1809.02859/) (2018)
- [Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach](https://mlsanity.com/paper/2201.05932/) (2022)
- [A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges](https://mlsanity.com/paper/2111.00978/) (2021)
- [Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach](https://mlsanity.com/paper/2111.12322/) (2021)
- [Method for Extracting Patterns of Coordinated Network Attacks on Electric Power CPS based on Temporal-Topological Correlation](https://mlsanity.com/paper/2003.10440/) (2020)
- [PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning](https://mlsanity.com/paper/2308.03953/) (2023)
- [Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process](https://mlsanity.com/paper/1808.05708/) (2018)
- [Optimal distributed generation planning in active distribution networks considering integration of energy storage](https://mlsanity.com/paper/1808.05712/) (2018)
- [Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors](https://mlsanity.com/paper/1810.08652/) (2018)
- [Optimal Power Flow for AC/DC System Based on Cooperative Multi-objective Particle Swarm Optimization](https://mlsanity.com/paper/1809.05232/) (2018)
- [Data Driven based Dynamic Correction Prediction Model for NOx Emission of Coal Fired Boiler](https://mlsanity.com/paper/2110.15600/) (2021)
- [Dynamic Exploitation Gaussian Bare-Bones Bat Algorithm for Optimal Reactive Power Dispatch to Improve the Safety and Stability of Power System](https://mlsanity.com/paper/2112.06382/) (2021)
- [A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning](https://mlsanity.com/paper/2203.07075/) (2022)
- [Auto-Encoder-Extreme Learning Machine Model for Boiler NOx Emission Concentration Prediction](https://mlsanity.com/paper/2206.14496/) (2022)
- [Image encryption for Offshore wind power based on 2D-LCLM and Zhou Yi Eight Trigrams](https://mlsanity.com/paper/2306.09245/) (2023)

## Abstract (arXiv)

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.