Abstract
This 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.
Highlights
Several countries with cold climates are using electric baseboards as household heating systems.These appliances, which consume a high amount of energy, require an energy management structure to monitor and control their power demands without jeopardizing users’ comfort
Seeking a robust and adaptive method that supports the dynamics of electric heaters, we propose a combined difference hidden Markov model (DHMM) with a reduced number of parameters
These results indicate the promise of the proposed approach to achieve a valid disaggregation practice even if the power profile is complex, with several electronic thermostats
Summary
Several countries with cold climates are using electric baseboards as household heating systems. These appliances, which consume a high amount of energy, require an energy management structure to monitor and control their power demands without jeopardizing users’ comfort. Most reported studies have focused on estimating heating-system demand through modeling the residential building’s thermal behavior [1,2,3]. In this regard, inverse modeling approaches based on prior knowledge of the environmental and household interior temperatures have been considered. In the cold season, opened entries, that is, Energies 2018, 11, 88; doi:10.3390/en11010088 www.mdpi.com/journal/energies
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