Machine Learning Approaches to Non-Intrusive Load Monitoring

Book Description

Research on Smart Grids has recently focused on the energy monitoring issue, with the objective of maximizing the user consumption awareness in building contexts on the one hand, and providing utilities with a detailed description of customer habits on the other. In particular, Non-Intrusive Load Monitoring (NILM), the subject of this book, represents one of the hottest topics in Smart Grid applications. NILM refers to those techniques aimed at decomposing the consumption-aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the system under study.

This book provides a status report on the most promising NILM methods, with an overview of the publically available dataset on which the and experiments are based. Of the proposed methods, those based on the Hidden Markov Model (HMM) and the Deep Neural (DNN) are the best performing and most interesting from the future improvement point of view. One method from each category has been selected and the improvements achieved are described. Comparisons are made between the two reference techniques, and pros and cons are considered. In addition, improvements can be achieved when the reactive power component is exploited in addition to the active power consumption trace.

Book Details

  • Title: Machine Learning Approaches to Non-Intrusive Load Monitoring
  • Author: ,
  • Length: 135 pages
  • Edition: 1st ed. 2020
  • Language: English
  • Publisher:
  • Publication Date: 2019-12-16
  • ISBN-10: 3030307816
  • ISBN-13: 9783030307813
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