电力需求侧管理2026，Vol.28Issue(1)：8-16,9.DOI:10.3969/j.issn.1009-1831.2026.01.002
基于MPA-CNN-LSTM融合模型与置信区间修正的行业用户负荷潜力评估
Industry user load potential assessment based on MPA-CNN-LSTM fusion model and confidence interval correction
摘要
Abstract
Against the backdrop of"dual-carbon"goals,growing installed capacity of new energy,changes in user load characteristics and increased load demand have intensified pressure on grid supply-demand balance.To maintain grid stability and fully tap the adjustable load potential of industrial users,an industrial user load potential assessment strategy based on a hybrid MPA-CNN-LSTM model com-bined with confidence interval correction is proposed.First,building on existing load characteristics,load reduction characteristics are introduced—describing the types and methods of load reduction among different users in the same industry—as inputs to the MPA-CNN-LSTM prediction model.Second,the MPA-optimized CNN-LSTM neural network is trained using actual adjustable potential data from responsive users to predict industrial users'adjustable potential.Finally,the confidence interval correction method is applied to re-fine the predicted adjustable potential,enhancing accuracy.关键词
负荷削减特性/MPA算法优化/CNN-LSTM/置信区间修正/潜力评估Key words
load shedding characteristics/MPA algorithm optimisation/CNN-LSTM/confidence interval correction/potential assessment分类
信息技术与安全科学引用本文复制引用
沈聪,艾芊,李晓露,高扬,陶伟健,赵晨阳..基于MPA-CNN-LSTM融合模型与置信区间修正的行业用户负荷潜力评估[J].电力需求侧管理,2026,28(1):8-16,9.基金项目
国家重点研发计划(2021YFB2401203) （2021YFB2401203）
国家自然科学基金(52407127) （52407127）
上海浦江人才计划(24PJA045) （24PJA045）
