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Energy disaggregation refers to the decomposition of energy use time seriesdata into its constituent loads. This paper decomposes daily use data of ahousehold unit into fixed loads and one or more classes of shiftable loads. Thelatter is characterized by ON OFF duty cycles. A novel algorithm based onnonnegative matrix factorization NMF for energy disaggregation is proposed,where fixed loads are represented in terms of real-valued basis vectors,whereas shiftable loads are divided into binary signals. This binarydecomposition approach directly applies L0 norm constraints on individualshiftable loads. The new approach obviates the need for more computationallyintensive methods e.g. spectral decomposition or mean field annealing that havebeen used in earlier research for these constraints. A probabilistic frameworkfor the proposed approach has been addressed. The proposed approach seffectiveness has been demonstrated with real consumer energy data.

An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads