Smart Home Energy Management (SHEM) systems can introduce adjustments in the working period and operations of the home appliances to allow for energy cost savings, which can however affect the Quality of Experience (QoE) perceived by the user. This paper analyses this issue and proposes a QoE-aware SHEM system, which relies on the knowledge of the annoyance suffered by the users when the operations of appliances are changed with respect to the ideal user's preferences. Accordingly, a number of profiles which describe different usages are created in the design phase. At the deployment stage, users behavior and annoyance are registered to assign one of these profiles per appliance. The assigned profile is then exploited by the QoE-aware Cost Saving Appliance Scheduling and the QoEaware Renewable Source Power Allocation algorithms. The former is aimed at scheduling controlled loads based on users profile preferences and electricity prices making use of a greedy approach. The latter re-allocates appliances' operations whenever a surplus of energy has been made available by renewable energy sources. Experimental results demonstrate that the annoyance perceived by the users is severely diminished with respect to a QoE-unaware strategy, at the expenses of only a limited reduction in energy saving.

Smart home energy management including renewable sources: A QoE-driven Approach

Pilloni, Virginia;Floris, Alessandro;Meloni, Alessio;Atzori, Luigi
2018

Abstract

Smart Home Energy Management (SHEM) systems can introduce adjustments in the working period and operations of the home appliances to allow for energy cost savings, which can however affect the Quality of Experience (QoE) perceived by the user. This paper analyses this issue and proposes a QoE-aware SHEM system, which relies on the knowledge of the annoyance suffered by the users when the operations of appliances are changed with respect to the ideal user's preferences. Accordingly, a number of profiles which describe different usages are created in the design phase. At the deployment stage, users behavior and annoyance are registered to assign one of these profiles per appliance. The assigned profile is then exploited by the QoE-aware Cost Saving Appliance Scheduling and the QoEaware Renewable Source Power Allocation algorithms. The former is aimed at scheduling controlled loads based on users profile preferences and electricity prices making use of a greedy approach. The latter re-allocates appliances' operations whenever a surplus of energy has been made available by renewable energy sources. Experimental results demonstrate that the annoyance perceived by the users is severely diminished with respect to a QoE-unaware strategy, at the expenses of only a limited reduction in energy saving.
customer comfort; k-means; quality of experience; renewable energy sources; smart home energy management; computer science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/198060
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