Non-intrusive load disaggregation method based on cascade broad learning and sparrow algorithm
Deep learning has been widely used in non-intrusive load disaggregation.Despite its high disaggregation accuracy,its problems such as complex network structure,extremely time-consuming training process,and some requirements for computational resources make it difficult to integrate with embedded equipment.To address these problems,a non-intrusive load disaggregation method based on cascaded broad learning and sparrow optimization was proposed for low-frequency data.Firstly,the connection method of the broad learning feature nodes was improved and the cascade broad learning load disaggregation network of each target device was constructed.Then,the optimal number of feature nodes and enhanced nodes of the disaggregation network of each target device was determined by the sparrow optimization algorithm to realize the efficient disaggregation of non-intrusive load.Finally,simulation experiments based on the real dataset UK-DALE were carried out,and the superiority of the proposed method was verified by comparing it with the commonly-used non-intrusive load disaggregation methods.
non-intrusiveload disaggregationbroad learningsparrow algorithmcascade of feature nodes
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