Estimating human interactions with electrical appliances for activity-based energy savings recommendations

Since the power consumption of different electrical appliances in a household can be recorded by individual smart meters, it becomes possible to start considering in more detail the interactions of the residents with those devices throughout the day. Appliances' usages should not be considered as independent events, but rather as enablers for activities. Leveraging activity knowledge over time will allow us to design personalized energy efficient measures. We envision the design of future ambient intelligence systems, where the smart home can optimize the energy consumption in regards to the lifestyles of its residents and the smart grid's needs. In this work, we propose an automated method for determining when an electrical device is triggered by households' residents solely from its power trace. Knowing when an appliance is in use is required for identifying recurrent patterns that could later be understood as activities.

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