# Context-Based Energy Disaggregation in Smart Homes
## 1 Introduction  
Achieving greater energy efficiency through ICT has become an increasingly relevant research topic in the last decade. With the steady rise in consumption and the decreasing availability of energy resources, a remarkable slowing down in energy wasting, especially through the widespread adoption of energy saving solutions, is increasingly targeted. It is expected that proper use of ICT (e.g., sensing, processing and actuation capabilities) would facilitate the achievement of this objective, in both domestic and industrial domains. The private home domain especially absorbs a non-negligible percentage of the energy demand. Indeed, domestic consumptions represent approximately one third of the whole energy usage in the European Union [1] as well as in the United States [2]. Several studies on domestic consumption habits [3; 4], have shown that often users are not aware of how much energy is consumed by the devices they use. It has been recognized [5] that this may impair the understanding and adoption of energy saving behaviors. In other words, if the user wereinformed about how much a specific device affects total consumption, he might change his behavior in order to save energy as well as money. Hence, in this context, the introduction of Load Monitoring techniques, which support the continuous monitoring of electricity consumption and the consequent analysis of measured data, can also help in providing end-users with information and suggestions for improving their consumption behavior. Load monitoring techniques can be grouped into three categories:  
1. Non-Intrusive Load Monitoring (NILM) [6]: NILM refers to a family of techniques whose purpose is to derive the power consumption of a specific device from the whole-house consumption profile. 2. Hardware-based sub-metering: this technique is based on the deployment of a distributed system of low-cost metering devices (_i.e._, smart plugs attached onto household appliances) connected through a wireless and/or wired network infrastructure to a data collection module. 3. Adoption of smart appliances: this approach relies on the use of household appliances enhanced with sensing, processing and communication capabilities that can remotely be controlled and configured. Although the adoption of smart appliances would facilitate the user in implementing cost and energy actions, this approach is not likely to be put in place in the short term. Moreover, only a subset of devices are usually available as \"smart appliances\", such as TVs, dishwashers, and ovens. On the other side, smart plugs can be attached to almost any type of device. However, this approach can be resource demanding since a fine grained monitoring would require the use of a relevant number of smart plugs. In addition to the required financial commitment, the physical deployment might not be easy for fixed appliances (_i.e._, washing machine, dishwasher, refrigerator, _etc._) or the user may be bothered by the obligation to constantly attach a smart plug to every portable device (_i.e._, hair dryer, phone charger, laptop, _etc._). On the other hand, NILM approaches which are based on whole-house consumption information can be easily deployed by leveraging existing and widely adopted smart meters. Several NILM algorithms have been proposed in literature [7] to disaggregate the output of smart meters. Most of them need monitoring data sampled at high frequency (at least 1 GHz frequency). In real-world scenarios, this assumption may be resource demanding whether the computation is performed locally in a Home Energy Management System (where data storage and processing resource-intensive tasks are performed) or in a remote server (since a high amount of data has to be transferred). In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). On one hand, this design choice has the advantage of allowing the use of low-cost metering devices.