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157 / 2023-10-19 20:14:55
Optimization of BiLSTM for PV Output Prediction Based on Hybrid Bat Algorithm
BiLSTM; Hybrid Bat Algorithm; PV Output Forecast; Gray Correlation Analysis
1.Renewable energy system > (4)、Other topics in renewable energy
Final Paper
Mengxiang Ding / Southwest Jiaotong University；the School of Electrical Engineering
Wenli Fan / Southwest Jiaotong University
Zixuan Liu / Southwest Jiaotong University
Shengyong Ye / State Grid Sichuan Economic Research Institute
Yang Shengyuan / 西南交通大学
Selamawit Mesfin / 西南交通大学
摘要 -- 光伏发电受气候环境影响较大，具有较大的不确定性。准确的光伏出力预测可以降低其不确定性，提高电力系统的可靠性。该文提出一种基于混合蝙蝠算法（HBA）优化的双向长短期记忆神经网络（Bi-directionallongshorttermmemory，BiLSTM）的光伏输出预测模型。首先，采用灰色相关分析方法进行环境因子与光伏出力数据的相关性分析，将相关系数较高的环境因子作为预测网络的输入特征;其次，利用HBA优化BiLSTM网络中的最优参数，建立HBA⁃LSTM预测模型;最后，利用某一区域的实际数据进行预测分析，结果表明，与BiLSTM预测方法相比，本文所提方法的预测精度更高。

 
Important Date

  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)
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