Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer

TL;DR

This paper introduces the Cloud Model Improved Transformer (CMIT), a novel method combining cloud models and Transformers optimized with particle swarm algorithms, to enhance the accuracy and robustness of power load predictions in network clusters.

Contribution

The paper presents a new hybrid approach integrating cloud models with Transformer architecture, optimized by particle swarm algorithms, for improved power load forecasting.

Findings

CMIT outperforms standard Transformer models in accuracy.

The method effectively manages uncertainties in load data.

Experimental validation on 31 datasets confirms its robustness.

Abstract

Load data from power network clusters indicates economic development in each area, crucial for predicting regional trends and guiding power enterprise decisions. The Transformer model, a leading method for load prediction, faces challenges modeling historical data due to variables like weather, events, festivals, and data volatility. To tackle this, the cloud model's fuzzy feature is utilized to manage uncertainties effectively. Presenting an innovative approach, the Cloud Model Improved Transformer (CMIT) method integrates the Transformer model with the cloud model utilizing the particle swarm optimization algorithm, with the aim of achieving robust and precise power load predictions. Through comparative experiments conducted on 31 real datasets within a power network cluster, it is demonstrated that CMIT significantly surpasses the Transformer model in terms of prediction accuracy, thereby highlighting its effectiveness in enhancing forecasting capabilities within the power network cluster sector.