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Research Article

Distributed Photovoltaic Power Energy Generation Prediction Based on Improved Multi-objective Particle Algorithm

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  • @ARTICLE{10.4108/ew.8901,
        author={Yuanzheng Xiao and Huawei Hong and Feifei Chen and Xiaorui Qian and Ming Xu and Hanbin Ma},
        title={Distributed Photovoltaic Power Energy Generation Prediction Based on Improved Multi-objective Particle Algorithm},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={12},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2025},
        month={3},
        keywords={Particle Algorithm, Distributed photovoltaic power generation, Power prediction, Long short-term memory network, Intelligent Power Grid},
        doi={10.4108/ew.8901}
    }
    
  • Yuanzheng Xiao
    Huawei Hong
    Feifei Chen
    Xiaorui Qian
    Ming Xu
    Hanbin Ma
    Year: 2025
    Distributed Photovoltaic Power Energy Generation Prediction Based on Improved Multi-objective Particle Algorithm
    EW
    EAI
    DOI: 10.4108/ew.8901
Yuanzheng Xiao1,*, Huawei Hong1, Feifei Chen1, Xiaorui Qian1, Ming Xu1, Hanbin Ma2
  • 1: State Grid Fujian Marketing Service Center
  • 2: State Grid Info-Telecom Great Power Science and Technology Co Ltd
*Contact email: huangf0305@163.com

Abstract

Accurate prediction of distributed photovoltaic (DPV) power generation is crucial for stable grid operation, yet existing methods struggle with the non-linear, intermittent nature of solar power, and traditional machine learning models face hyperparameter selection and overfitting challenges. This study developed a highly accurate DPV power prediction method by optimizing a Long Short-Term Memory (LSTM) network's hyperparameters using an improved Multi-Objective Particle Swarm Optimization (MO-PSO) algorithm. A hybrid LSTM-PSO model was created, where the LSTM network served as the core prediction model, and the improved MO-PSO algorithm optimized its hyperparameters, enhancing generalization and avoiding overfitting. The LSTM-PSO model significantly improved prediction accuracy compared to traditional methods. Key results from two power stations included a maximum deviation of 6.2 MW at Power Station A, a peak time deviation of less than 0.1 MW at Power Station B, and a prediction interval error controlled below 30 MW at an 80% confidence level. The optimized LSTM-PSO model effectively captures DPV power generation dynamics, and the superior performance metrics demonstrate its potential for intelligent grid management. However, limitations include prediction accuracy under extreme weather and computational efficiency for large datasets. Future work will focus on broader applicability and more efficient algorithm variants.

Keywords
Particle Algorithm, Distributed photovoltaic power generation, Power prediction, Long short-term memory network, Intelligent Power Grid
Received
2024-12-20
Accepted
2025-03-09
Published
2025-03-13
Publisher
EAI
http://dx.doi.org/10.4108/ew.8901

Copyright © 2025 Y. Xiao et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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