Research on Non-Intrusive Load Monitoring Method Based on Manifold Feature Extraction of UMAP and KELM
Non-intrusive load monitoring is a key technology for smart data mining on the user side of the"strong smart grid".To address the problem of low accuracy of existing identification algorithms for superimposed state load,a non-intrusive load identification model based on the combination of uniform manifold approximation and projection(UMAP)and KELM is proposed.Firstly,UMAP is used to embed the original load features,extract the intra-class manifold structure of the load,and combine with stochastic gradient descent to optimize the global structure of the load,which effectively increases the distinguishability of the load features while retaining the original adjacent position information of the load.Then the kernel mapping network is constructed using radial basis functions,and the ACO algorithm is used to optimize the radial range of the mapping network and the penalty coefficients of the model to establish the optimal identification model.Compared with other machine learning-based identification methods,the proposed model achieves significant improvement in the identification accuracy of superimposed state load,reaching 98.48%and 99.44%on the TIPDM and BLUED datasets,respectively.
non-intrusive load monitoringsuperimposed state loadUMAPACOKELM