电力需求侧管理2025，Vol.27Issue(3)：38-43,6.DOI:10.3969/j.issn.1009-1831.2025.03.006
基于CNN-Attention-BiLSTM的碳化硅企业负荷预测与可调节潜力分析
Load forecasting and adjustable potential analysis of silicon carbide enterprises based on CNN-Attention-BiLSTM
摘要
Abstract
Industrial load accounts for a large proportion of social electricity consumption,and the adjustable load resources are abundant,so it is imperative to analyze its adjustable potential.Due to the large change rate of industrial load and many load tips,it is difficult to pre-dict the adjustable potential in real time.Therefore,the silicon carbide industry of a typical industrial enterprise is selected to establish an adjustable potential deduction model.First,the impact of weather characteristics and electricity price factors on the enterprise load is con-sidered through the Person correlation analysis method.At the same time,the Bi-directional long short-term memory(BiLSTM)prediction model processed by convolutional neural network(CNN)and Attention mechanism is established.The adjustable potential of silicon car-bide enterprises is explored by using the model prediction results.In order to verify the effectiveness of this method,this algorithm is signif-icantly superior to other comparison algorithms by establishing different algorithms for comparison and the tunable potential results under different strategies.Meanwhile,the tunable potential results of the three strategies can deepen the power grid's understanding of the load characteristics of such enterprises.关键词
负荷预测/可调节潜力/碳化硅企业/注意力机制/卷积神经网络/双向长短期记忆网络Key words
load forecasting/adjustable potential/silicon carbide enterprises/Attention mechanism/convolutional neural network/Bi-di-rectional long short-term memory分类
动力与电气工程引用本文复制引用
任明远,马国瀚,唐聪,曹万雄,孟涛,杨彤..基于CNN-Attention-BiLSTM的碳化硅企业负荷预测与可调节潜力分析[J].电力需求侧管理,2025,27(3):38-43,6.基金项目
国家重点研发计划资助项目(NO.2021YFB2401200) （NO.2021YFB2401200）
