Hybrid EEMD-WOA-LSTM model for enhanced power load prediction

Yuwei Chen; Jing Zhou; Shuang Lin; Wenxu Yao

doi:10.1117/12.3051845

18 November 2024 Hybrid EEMD-WOA-LSTM model for enhanced power load prediction

Yuwei Chen, Jing Zhou, Shuang Lin, Wenxu Yao

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033N (2024) https://doi.org/10.1117/12.3051845
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

This paper investigates power load forecasting models and introduces an advanced method that integrates Ensemble Empirical Mode Decomposition (EEMD), Whale Optimization Algorithm (WOA), and Long Short-Term Memory neural network (LSTM). The proposed EEMD-WOA-LSTM model is validated through experiments, demonstrating superior prediction accuracy and efficiency compared to traditional methods, particularly in managing nonlinear and non-stationary data. The model outperforms traditional LSTM and WOA-LSTM models in various evaluation metrics, showing a closer alignment with actual values when handling complex load data. Additionally, the paper discusses future research directions, such as model optimization, multi-source data fusion, real-time prediction systems, and extending applications to broader areas. This study not only highlights the significant potential of the EEMD-WOA-LSTM model in power load forecasting but also offers valuable technical references for the intelligent transformation of the power industry, aiming to develop more advanced and practical solutions for smart grids and related fields.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuwei Chen, Jing Zhou, Shuang Lin, and Wenxu Yao "Hybrid EEMD-WOA-LSTM model for enhanced power load prediction", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033N (18 November 2024); https://doi.org/10.1117/12.3051845

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