Monitoring Daily Activities in Households by Means of Energy Consumption Measurements from Smart Meters

Abstract

Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building, where signals of interest, such as voltage or current, can be measured and analyzed in order to disaggregate and identify which appliance is turned on/off at any time. Although this information is key for further applications linked to energy efficiency and management, it may also be applied to social and health contexts. Since the activation of the appliances in a household is related to certain daily activities carried out by the corresponding tenants, NILM techniques are also interesting in the design of remote monitoring systems that can enhance the development of novel feasible healthcare models. Therefore, these techniques may foster the independent living of elderly and/or cognitively impaired people in their own homes, while relatives and caregivers may have access to additional information about a person’s routines. In this context, this work describes an intelligent solution based on deep neural networks, which is able to identify the daily activities carried out in a household, starting from the disaggregated consumption per appliance provided by a commercial smart meter. With the daily activities identified, the usage patterns of the appliances and the corresponding behaviour can be monitored in the long term after a training period. In this way, every new day may be assessed statistically, thus providing a score about how similar this day is to the routines learned during the training interval. The proposal has been experimentally validated by means of two commercially available smart monitors installed in real houses where tenants followed their daily routines, as well as by using the well-known database UK-DALE.