A Non-Intrusive Appliance Load Monitor (NIALM) refers to a device that can determine the operational state of connected electrical appliances from a single point of measurement. It aims to provide the advantages of sub-monitoring approaches without incurring the cost or intrusiveness of installation.
The output from a NIALM can empower the consumer by providing a breakdown of how much electrical energy individual appliances use. This can directly highlight energy savings through change in lifestyle or appliance replacement. Additionally, it can provide the input necessary should an agent seek to control appliances in the home in an energy efficient manner. Ultimately, this small-scale reduction in energy use contributes towards the global challenge of minimising energy generation and greenhouse-gas emissions.
Previous solutions to the field of NIALM utilise high-granularity electrical readings, typically collected using custom-built meter. Approaches generally use signal processing and machine learning techniques to disaggregate the combined readings to give a break-down by appliance.
However, there is little research on whether such approaches can be applied to low-granularity data. Such low-granularity data will be measured by modern smart meters, which will become ubiquitous within UK homes by 2020.
This research focuses on the application of NIALM techniques to low-granularity data that could be collected from smart meters. It aims to enrich machine learning NIALM techniques using additional data sources such as collaborative appliance models, and temporal, user and environmental data.
The order of this work is as follows. First, the goals and applications will be stated. An in depth discussion of related literature and its limitations will then be given, followed by a description of our contribution. Our proposed approach will then be evaluated against existing approaches before the conclusions are highlighted and future work is suggested.