Abstract
In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. 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). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments.
Highlights
Achieving greater energy efficiency through ICT has become an increasingly relevant research topic in the last decade
Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device
We propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency
Summary
Achieving greater energy efficiency through ICT has become an increasingly relevant research topic in the last decade. 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. Appliance Profiling refers to the observation of an electronic device’s consumption behavior in order to extract all the features that could characterize it in detail. It consists of defining a set of relations between the working states of an appliance and the energy that it consumes [11]. Devices that are permanently on and are characterized by an almost constant power trace (e.g., smoke alarms, telephones, etc.)
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