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Machine Learning Approaches to Non-Intrusive Load Monitoring (Paperback, 1st ed. 2020)
Loot Price: R1,557
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Machine Learning Approaches to Non-Intrusive Load Monitoring (Paperback, 1st ed. 2020)
Series: SpringerBriefs in Energy
Expected to ship within 10 - 15 working days
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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 electrical 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
algorithm and experiments are based. Of the proposed methods, those
based on the Hidden Markov Model (HMM) and the Deep Neural Network
(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 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.
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