Exploring the Performance of Non-negative Multi-way Factorization for Household Electrical Seasonal Consumption Disaggregation



The performance of household electrical seasonal consumption disaggregation is explored in this paper. Firstly, given a tensor composed by the data for the several devices in the house, non-negative tensor factorization is performed in order to extract the most relevant components. Secondly, the outcome is embedded in the test step, where only the whole-home measured consumption is available. Lastly, the disaggregated data by device is obtained by factorizing the associated matrix regarding the learned model. This source separation approach thus requires prior data, needed to learn the source models. Nevertheless, the consumer behaviors vary along time particularly from season to season, and hence also the electrical consumption. Consequently, the assessment of performance at long-term and across different times of the year is essential. We evaluate the performance of load disaggregation by this supervised method along several years and across seasons. Towards this end, computational experiments were yielded using real-world data from a household electrical consumption measurements along several years. The analysis of the computational results illustrates the adequacy of the method for handling the shifts between seasons.


Electrical Signal Disaggregation, Non-Intrusive Load Monitoring, Non-negative Tensor Factorization, Single-Channel Source Separation


Non-Intrusive Load Monitoring Systems


IEEE International Joint Conference on Neural Networks (IJCNN) , July 2014

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