电力需求侧管理2025，Vol.27Issue(2)：48-54,7.DOI:10.3969/j.issn.1009-1831.2025.02.008
考虑多模态分解的融合模型空调负荷可调潜力分析
Air conditioning load adjustable potential analysis of fusion model considering multimodal decomposition
摘要
Abstract
Flexible load resources can respond to power grid dispatching quickly without significant impact on user comfort because of their rapid response and flexible regulation.As the core part of flexible load,air conditioning load can reduce the peak power demand through scientific control strategy,and then relieve the pressure of power supply.In view of the nonlinear and fuzzy characteristics of air conditioning load data,a model of air conditioning load prediction based on modal decomposition and neural network is proposed.First,Pearson correlation coefficients are used to construct similar weekly load sequences.Then the load is decomposed by adaptive noise com-plete set empirical mode decomposition and variational mode decomposition(VMD).In the VMD section,the original time series signal is input into the VMD layer and decomposed into multiple eigenmode functions(IMFs)by the VMD algorithm.These IMFs are input into convolutional neural network respectively,and their local features are extracted by convolutional,activation and pooling operations.These feature vectors are then fed into a bidirectional long short-term memory network,which uses its bidirectional propagation capability to cap-ture long-term dependencies in the sequence.The improved whale algorithm is used to optimize the hyperparameters,and the regulation potential of the load is further discussed on the basis of the output forecast load sequence.The experimental results show that this method not only has high forecasting speed and accuracy,but also can reveal the adjustment potential of load more clearly.关键词
柔性负荷/模态分解/鲸鱼算法/卷积神经网络/双向长短期记忆网络Key words
flexible load/modal decomposition/whale algorithm/CNN/BiLSTM分类
动力与电气工程引用本文复制引用
宫飞翔,陈宋宋,罗鑫宇,李彬..考虑多模态分解的融合模型空调负荷可调潜力分析[J].电力需求侧管理,2025,27(2):48-54,7.基金项目
国家电网有限公司科技项目(5108-202218280A-2-389-XG) （5108-202218280A-2-389-XG）
