河北科技大学学报2025，Vol.46Issue(1)：41-48,8.DOI:10.7535/hbkd.2025yx01005
基于模糊逻辑的FBiLSTM-Attention短期负荷预测
FBiLSTM-Attention short-term load forecasting based on fuzzy logic
摘要
Abstract
Aiming at the problem of high uncertainty in power load data due to various factors,a fuzzy logic based FBiLSTM Attention short-term load forecasting model was proposed by combining the uncertainty of load data with deep learning algorithms to improve the accuracy of load forecasting.Firstly,the raw data,including filling in missing values,conducting correlation analysis and normalizing the data,was preprocessed.Secondly,K-Means clustering was used to transform the data of each feature into fuzzy rules and introduce fuzzy logic processing.In terms of model structure,a bi-directional long short-term memory(BiLSTM)and attention mechanism(Attention)were adopted.Finally,the prediction results of the proposed method with traditional LSTM and BiLSTM Attention models were compared.The results show that the model combined with fuzzy logic has significantly improved accuracy and robustness,and has better predictive performance.The proposed model can effectively improve the ability to handle uncertain data,providing reference for load forecasting study.关键词
数据处理/模糊逻辑/负荷预测/双向长短期记忆网络/注意力机制Key words
data processing/fuzzy logic/load forecasting/bi-directional long short-term memory(BiLSTM)/attention mechanism(Attention)分类
信息技术与安全科学引用本文复制引用
张岩,康泽鹏,高晓芝,杨楠,王昭雷..基于模糊逻辑的FBiLSTM-Attention短期负荷预测[J].河北科技大学学报,2025,46(1):41-48,8.基金项目
国家自然科学基金(62233006) （62233006）
河北省高等学校科学技术研究项目(ZD2021202,QN2022028) （ZD2021202,QN2022028）
