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基于小波變換結合堆疊融合算法的非侵入式負載識別

來源：電工電氣發布時間：2025-10-28 15:28瀏覽次數：34

基于小波變換結合堆疊融合算法的非侵入式負載識別

李港，邱達，劉西林

(湖北民族大學智能科學與工程學院，湖北恩施 445000)

摘要：針對非侵入式負載監測識別準確率低、泛化能力弱、穩定性差的問題，提出了一種結合特征選擇性小波變換與堆疊融合分類算法的負載識別方法。研究利用 CS5463 芯片采集電能數據，通過特征選擇性小波變換提取電流的時頻特征，并結合功率和功率因數構建復合特征向量。采用k 最近鄰算法(KNN)、隨機森林(RF)和支持向量機(SVM)作為基學習器，通過堆疊融合算法提升準確率、泛化能力，優化分類性能，并引入動態負載識別優化算法以提升實際應用效果。實驗結果表明，該堆疊融合模型在測試集上的準確率為98.42%，而單一模型KNN、SVM和RF的準確率分別為90.24%、94.99% 和97.10%，同樣數據集上未經小波變換的融合算法準確率為93.67%，加入動態負載識別優化算法后，模型的穩定性和準確性在實際應用中進一步提高。

關鍵詞： 非侵入式負載監測；特征選擇性小波變換；堆疊融合算法；CS5463 芯片；動態負載識別優化算法

中圖分類號：TM714 ；TM734 文獻標識碼：A 文章編號：1007-3175(2025)10-0031-07

A Non-Intrusive Load Identification Method Based on Wavelet

Transform and Stacked Fusion Algorithm

LI Gang, QIU Da, LIU Xi-lin

（College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China）

Abstract: To address the challenges of low identification accuracy, weak generalization capability, and poor stability in non-intrusive load monitoring,this paper proposes a load identification method that integrates feature-selective wavelet transform with a stacked fusion algorithm. The study utilizes the CS5463 chip to collect electrical data, extracts the time-frequency characteristics of current signals by applying feature-selective wavelet transform, and combines with power and power factor information to construct a composite feature vector. Subsequently, k-nearest neighbors (KNN) algorithm, random forests (RF) , and support vector machines (SVM) are employed as base learners, the accuracy and generalization ability are enhanced through the stacked fusion algorithm, the classification performance is optimized, and the dynamic load identification optimization algorithm is introduced to improve the practical application effect. Experimental results demonstrate that the accuracy rate of the stacked fusion model on the test set is 98.42%, while the accuracy rates of the single models KNN, SVM and RF are 90.24%, 94.99% and 97.10% respectively. The accuracy rate of the fusion algorithm without wavelet transform on the same dataset is 93.67%. After adding the dynamic load identification optimization algorithm,the stability and accuracy of the model have been further enhanced in practical applications.

Key words: non-intrusive load monitoring; feature-selective wavelet transform; stacked fusion algorithm; CS5463 chip; dynamic load identification optimization algorithm

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基于小波變換結合堆疊融合算法的非侵入式負載識別