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Machine Learning Approaches to Non-Intrusive Load Monitoring

Produktform: E-Buch Text Elektronisches Buch in proprietärem

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, , the subject of this book, . 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 electrical system under study.  One method from each category has been selected and the performance improvements achieved are described. Comparisons are made between the two reference techniques, and pros and cons are considered. In addition, performance improvements can be achieved when the reactive power component is exploited in addition to the active power consumption trace.weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Elektronisches Format: PDF

Sprache(n): Englisch

ISBN: 978-3-030-30782-0 / 978-3030307820 / 9783030307820

Verlag: Springer International Publishing

Erscheinungsdatum: 01.11.2019

Seiten: 135

Autor(en): Stefano Squartini, Roberto Bonfigli

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