Analysis of Trends in Seasonal Electrical Energy Consumption via Non-negative Tensor Factorization,



This paper looks at the extraction of trends of household electrical seasonal consumption via load disaggregation. With the proviso that data for several home devices can be embedded in a tensor, non-negative multi-way array factorization is performed in order to extract the most relevant components. In the initial decomposition step the decomposed signals are incorporated in the test signal consisting of the whole-home measured consumption. After this the disaggregated data corresponding to each electrical device is obtained by factorizing the associated matrix through the learned model. Finally, we evaluate the performance of load disaggregation by the supervised method, and study the trends along several years and across seasons. Towards this end, computational experiments were yielded using real-world data from household electrical consumption measurements along several years. While breaking down the whole house energy consumption into appliance level gives less accurate estimates in the late years, we empirically show the adequacy of this method for handling the earlier years and the estimates of the underlying seasonal trend-cycle.


Non-negative tensor factorization, Electrical signal disaggregation, Non-intrusive load monitoring (NILM), Energy efficiency


Neurocomputing, Elsevier, Vol. 170, #1, pp. 318-327, Elsevier, December 2015


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Year 2016 : 1 citations

 Yuankai Wua, Huachun Tana,Yong Lib, Feng Lic, Hongwen Hea. Robust tensor decomposition based on Cauchy distribution and its application, Neurocomputing, Volume 223(5): 107–117, 2017 (to appear)