Research on Short-Term Electric Load Forecasting Using IWOA CNN-BiLSTM-TPA Model  

基于IWOA的CNN-BiLSTM-TPA模型的短期电力负荷预测研究

作　　者：MEI Tong-da SI Zhan-jun ZHANG Ying-xue 梅通达;司占军;张滢雪(天津科技大学人工智能学院,天津300457)

机构地区：[1]College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China

出　　处：《印刷与数字媒体技术研究》2025年第1期179-187,共9页Printing and Digital Media Technology Study

摘　　要：Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.摘要负荷预测对于新型电力系统发展的意义重大。随着智能电网的发展,分布式可再生能源和电力电子器件的整合分配使得电力负荷数据变得愈发的复杂且波动,这给电力负荷的预测和分析提出了更高的要求。为了提高短期电力负荷的预测精度,本研究提出一种基于混合策略改进的鲸鱼优化算法(Improved Whale Optimization Algorithm,IWOA)的CNN-BiLSTM-TPA短期电力预测模型。首先,该模型将卷积神经网络(Convolutional Neural Network,CNN)和双向长、短期记忆网络(Bidirectional Long Short-Term Memory,BiLSTM)相结合充分提取负荷数据本身的时空特征。然后,在CNN-BiLSTM中引入时间注意力(Temporal Pattern Attention Mechanism,TPA)机制,自动为BiLSTM隐藏层状态分配相应的权重,以区分不同时间负荷序列的重要性。同时,为了解决时序模型的参数较难选择以及鲸鱼算法全局搜索能力差、易陷入局部最优等问题,本研究采用Tent混沌映射和Levy飞行策略来混合优化鲸鱼算法,以便于更好地对模型参数进行寻优。最后,本研究以浙江某地区真实负荷数据为例进行分析,并与BP、WOA-CNN-BiLSTM、SSA-CNNBiLSTM、CNN-BiGRU-Attention等预测模型进行对比。实验结果表明,本研究所提模型的预测精度(R2)达到了98.83%,其具有更高的预测精度。

关 键 词：Whale Optimization Algorithm Convolutional Neural Network Long Short-Term Memory Temporal Pattern Attention Power load forecasting 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]