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Figueiredo, M., Ribeiro, B. & de Almeida, A. (2015). Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization. Neurocomputing. 170, 318-327

M. B. Figueiredo et al.,  "Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization", in Neurocomputing, vol. 170, pp. 318-327, 2015

@article{figueiredo2015_1714262415089,
	author = "Figueiredo, M. and Ribeiro, B. and de Almeida, A.",
	title = "Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization",
	journal = "Neurocomputing",
	year = "2015",
	volume = "170",
	number = "",
	doi = "10.1016/j.neucom.2015.03.088",
	pages = "318-327",
	url = "http://www.sciencedirect.com/science/article/pii/S0925231215008620"
}

TY  - JOUR
TI  - Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization
T2  - Neurocomputing
VL  - 170
AU  - Figueiredo, M.
AU  - Ribeiro, B.
AU  - de Almeida, A.
PY  - 2015
SP  - 318-327
SN  - 0925-2312
DO  - 10.1016/j.neucom.2015.03.088
UR  - http://www.sciencedirect.com/science/article/pii/S0925231215008620
AB  - 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.
ER  -