Short-term power load prediction based on DBO-VMD and an IWOA-BILSTM neural network combination model

被引：0
|
作者：
Liu J. [1 ]
Cong L. [1 ]
Xia Y. [2 ]
Pan G. [1 ]
Zhao H. [1 ]
Han Z. [1 ]
机构：
[1] School of Automation and Electrical Engineering, Linyi University, Linyi
[2] Yalong River Basin Hydropower Development Co., Ltd., Chengdu
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2024年 / 52卷 / 08期
基金：
中国国家自然科学基金;
关键词：
bidirectional long and short-term memory neural networks (BILSTM); combinatorial algorithms; dung beetle optimization (DBO) algorithm; improved whale algorithm; short-term electric load prediction; VMD;
D O I：
10.19783/j.cnki.pspc.231402
中图分类号：
学科分类号：
摘要：
The share of renewable energy in modern power systems is increasing, causing its load to fluctuate more erratically than in conventional power systems. This volatility leads to lower accuracy of load prediction. To address this issue, this paper introduces a short-term load prediction model combining the dung beetle optimization algorithm (DBO) with optimized variational mode decomposition (VMD) and an improved whale optimization algorithm to optimize bidirectional long short-term memory (IWOA-BILSTM) neural networks. The DBO is used to optimize the VMD, the time series data is decomposed, and various feature data are classified according to the minimum envelope entropy. This enhances the decomposition effect. The fluctuation of the data is reduced by effectively decomposing the original data. Then the whale optimization algorithm is improved using a nonlinear convergence factor, adaptive weight strategy and random difference variation strategy to enhance the local and global search ability of the whale optimization algorithm. Thus an improved whale optimization algorithm (IWOA) is obtained, and it is then used to optimize bidirectional long short-term memory (BILSTM) neural networks, increasing the accuracy of model predictions. Finally, this method is tested on real load data from a location, yielding favorable results. The resulting metrics for relative root mean square, mean absolute and mean absolute percentage errors are recorded at 0.0084, 48.09, and 0.66%, respectively. These outcomes verify the effectiveness of the proposed model in short-term load prediction. © 2024 Power System Protection and Control Press. All rights reserved.
引用
收藏
页码：123 / 133
页数：10
相关论文
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