Abstract
The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.
Highlights
Non-Intrusive Load Monitoring (NILM) techniques have become one of the most relevant alternatives for energy disaggregation, since they provide a method to separate the individual consumption for certain appliances, respecting consumers’ privacy and often using already-deployed smart meters
The rise of these NILM techniques has been fostered by the recent importance of some emerging domains, such as Internet of Things (IoT), Smart Grids (SG) or Demand Response (DR)
Employing day-ahead pricing information, their system is composed of five modules: energy production, which consists of solar radiation and air temperature predictors, used to forecast the PhotoVoltaic (PV) energy generation; solar energy management, which manages the flow of energy between the grid, PV and battery storage; NILM module, which disaggregates the energy and estimates consumptions, and computes usage patterns and features of each appliance; classifier, which labels the appliances as schedulable or not and, in the former case, passes this information, together with adjustable ranking, to the module; and appliances scheduling, which, based on the information received from the previous module for deferrable appliances, proposes a dynamic algorithm to determine which state sequences in a certain appliance provide a lowest electricity cost over time
Summary
Non-Intrusive Load Monitoring (NILM) techniques have become one of the most relevant alternatives for energy disaggregation, since they provide a method to separate the individual consumption for certain appliances, respecting consumers’ privacy and often using already-deployed smart meters. The rise of these NILM techniques has been fostered by the recent importance of some emerging domains, such as Internet of Things (IoT), Smart Grids (SG) or Demand Response (DR). NILM can provide information about activities within the home, and has become an emerging alternative to be used in health and care applications In this case, again, non-intrusiveness is the main and crucial advantage for NILM. A summary of the most important characteristics of the works referenced in this review is presented in the Appendix
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