Energy disaggregation refers to the decomposition of energy use time series data into its constituent loads. This paper decomposes daily use data of a household unit into fixed loads and one or more classes of shiftable loads. The latter is characterized by ON OFF duty cycles. A novel algorithm based on nonnegative 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 binary decomposition approach directly applies L0 norm constraints on individual shiftable loads. The new approach obviates the need for more computationally intensive methods e.g. spectral decomposition or mean field annealing that have been used in earlier research for these constraints. A probabilistic framework for the proposed approach has been addressed. The proposed approach s effectiveness has been demonstrated with real consumer energy data.