Non-intrusive Load Monitoring

  • Roberto Bonfigli
  • Stefano Squartini
Part of the SpringerBriefs in Energy book series (BRIEFSENERGY)


The issues relating to the energy conservation and efficiency have gained a role of great importance, from the point of view of both the consumer and the energy provider. Furthermore, over the years, the infrastructures for energy distribution have undergone an ageing process, which have led to the study of the possibility in smart grids implementation, in which a set of information from detection and network management systems can be transmitted in addition to energy.

Useful information, about the characteristics and operating behaviour of an electrical system, can be obtained by means of the power consumption analysis, in order to predict the power demand (load forecasting), to apply management policies and to avoid overloading or blackouts over the energy network. Similarly, from the user perspective, the lifestyle of the people in a house can be predicted by the energy consumption analysis, allowing to implement policies for advantageous time tariffs.

Over the years, several studies have demonstrated that the energy consumption awareness (i.e., which appliances are operating at a certain time instant and how much electrical power they are consuming) influences the user behaviour. Specifically, the awareness conducts to moderate energy consumption, resulting in monetary savings and reduction of the energy required to the provider. Furthermore, applying this consideration to commercial or industrial environments, it may provide larger energy saving.

In the struggle to improve the energy efficiency of residential environments, the availability of information about the appliances in use can support automated optimization approaches.

Load monitoring has become a challenging problem, and several techniques have been studied to solve it. This work is focused on Non-Intrusive Load Monitoring (NILM) algorithms, which aim to separate the aggregated energy consumption signal, measured in a single centralized point, in the individual signals from each appliance, using a simple hardware but smart software algorithms. This solution replaces a distributed smart socket grid inside the house, resulting in lower implementation costs and less invasive solutions for the end user.


Non-intrusive load monitoring State of the art Hidden Markov model Deep neural network Energy dataset Evaluation metric 


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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Roberto Bonfigli
    • 1
  • Stefano Squartini
    • 1
  1. 1.Marche Polytechnic UniversityVia Brecce Bianche 12Italy

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