Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach
AbstractThis paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability. View Full-Text
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Henao, N.; Agbossou, K.; Kelouwani, S.; Hosseini, S.S.; Fournier, M. Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach. Energies 2018, 11, 88.
Henao N, Agbossou K, Kelouwani S, Hosseini SS, Fournier M. Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach. Energies. 2018; 11(1):88.Chicago/Turabian Style
Henao, Nilson; Agbossou, Kodjo; Kelouwani, Sousso; Hosseini, Sayed S.; Fournier, Michael. 2018. "Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach." Energies 11, no. 1: 88.
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