2019/07/31 by Ahmad Khaled Zarabie, Zarabie, Ahmad Khaled, Sanjoy Das +1 · 1 citation
Computer Science · Engineering · #Blind Source Separation Techniques #Data Structures and Algorithms (cs.DS) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #Smart Grid Energy Management #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.00142
openalex publication_date 2019/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.