Energy Data Analytics for Smart Meter Data

doi:10.3390/books978-3-0365-2017-9

dc.contributor.editor	Reinhardt, Andreas
dc.contributor.editor	Pereira, Lucas
dc.date.accessioned	2022-01-11T13:45:21Z
dc.date.available	2022-01-11T13:45:21Z
dc.date.issued	2021
dc.identifier	ONIX_20220111_9783036520162_625
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/76890
dc.description.abstract	The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.
dc.language	English
dc.subject.classification	thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues	en_US
dc.subject.other	smart grid
dc.subject.other	nontechnical losses
dc.subject.other	electricity theft detection
dc.subject.other	synthetic minority oversampling technique
dc.subject.other	K-means cluster
dc.subject.other	random forest
dc.subject.other	smart grids
dc.subject.other	smart energy system
dc.subject.other	smart meter
dc.subject.other	GDPR
dc.subject.other	data privacy
dc.subject.other	ethics
dc.subject.other	multi-label learning
dc.subject.other	Non-intrusive Load Monitoring
dc.subject.other	appliance recognition
dc.subject.other	fryze power theory
dc.subject.other	V-I trajectory
dc.subject.other	Convolutional Neural Network
dc.subject.other	distance similarity matrix
dc.subject.other	activation current
dc.subject.other	electric vehicle
dc.subject.other	synthetic data
dc.subject.other	exponential distribution
dc.subject.other	Poisson distribution
dc.subject.other	Gaussian mixture models
dc.subject.other	mathematical modeling
dc.subject.other	machine learning
dc.subject.other	simulation
dc.subject.other	Non-Intrusive Load Monitoring (NILM)
dc.subject.other	NILM datasets
dc.subject.other	power signature
dc.subject.other	electric load simulation
dc.subject.other	data-driven approaches
dc.subject.other	smart meters
dc.subject.other	text convolutional neural networks (TextCNN)
dc.subject.other	time-series classification
dc.subject.other	data annotation
dc.subject.other	non-intrusive load monitoring
dc.subject.other	semi-automatic labeling
dc.subject.other	appliance load signatures
dc.subject.other	ambient influences
dc.subject.other	device classification accuracy
dc.subject.other	NILM
dc.subject.other	signature
dc.subject.other	load disaggregation
dc.subject.other	transients
dc.subject.other	pulse generator
dc.subject.other	smart metering
dc.subject.other	smart power grids
dc.subject.other	power consumption data
dc.subject.other	energy data processing
dc.subject.other	user-centric applications of energy data
dc.subject.other	convolutional neural network
dc.subject.other	energy consumption
dc.subject.other	energy data analytics
dc.subject.other	energy disaggregation
dc.subject.other	real-time
dc.subject.other	smart meter data
dc.subject.other	transient load signature
dc.subject.other	attention mechanism
dc.subject.other	deep neural network
dc.subject.other	electrical energy
dc.subject.other	load scheduling
dc.subject.other	satisfaction
dc.subject.other	Shapley Value
dc.subject.other	solar photovoltaics
dc.subject.other	review
dc.subject.other	deep learning
dc.subject.other	deep neural networks
dc.subject.other	n/a
dc.title	Energy Data Analytics for Smart Meter Data
dc.type	book
oapen.identifier.doi	10.3390/books978-3-0365-2017-9
oapen.relation.isPublishedBy	46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn	9783036520162
oapen.relation.isbn	9783036520179
oapen.pages	346
oapen.place.publication	Basel, Switzerland

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