Energy Data Analytics for Smart Meter Data
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 a...
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04427naaaa2201141uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_76890 | ||
| 005 | 20220111 | ||
| 020 | |a books978-3-0365-2017-9 | ||
| 020 | |a 9783036520162 | ||
| 020 | |a 9783036520179 | ||
| 024 | 7 | |a 10.3390/books978-3-0365-2017-9 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a TB |2 bicssc | |
| 100 | 1 | |a Reinhardt, Andreas |4 edt | |
| 700 | 1 | |a Pereira, Lucas |4 edt | |
| 700 | 1 | |a Reinhardt, Andreas |4 oth | |
| 700 | 1 | |a Pereira, Lucas |4 oth | |
| 245 | 1 | 0 | |a Energy Data Analytics for Smart Meter Data |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (346 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 653 | |a smart grid | ||
| 653 | |a nontechnical losses | ||
| 653 | |a electricity theft detection | ||
| 653 | |a synthetic minority oversampling technique | ||
| 653 | |a K-means cluster | ||
| 653 | |a random forest | ||
| 653 | |a smart grids | ||
| 653 | |a smart energy system | ||
| 653 | |a smart meter | ||
| 653 | |a GDPR | ||
| 653 | |a data privacy | ||
| 653 | |a ethics | ||
| 653 | |a multi-label learning | ||
| 653 | |a Non-intrusive Load Monitoring | ||
| 653 | |a appliance recognition | ||
| 653 | |a fryze power theory | ||
| 653 | |a V-I trajectory | ||
| 653 | |a Convolutional Neural Network | ||
| 653 | |a distance similarity matrix | ||
| 653 | |a activation current | ||
| 653 | |a electric vehicle | ||
| 653 | |a synthetic data | ||
| 653 | |a exponential distribution | ||
| 653 | |a Poisson distribution | ||
| 653 | |a Gaussian mixture models | ||
| 653 | |a mathematical modeling | ||
| 653 | |a machine learning | ||
| 653 | |a simulation | ||
| 653 | |a Non-Intrusive Load Monitoring (NILM) | ||
| 653 | |a NILM datasets | ||
| 653 | |a power signature | ||
| 653 | |a electric load simulation | ||
| 653 | |a data-driven approaches | ||
| 653 | |a smart meters | ||
| 653 | |a text convolutional neural networks (TextCNN) | ||
| 653 | |a time-series classification | ||
| 653 | |a data annotation | ||
| 653 | |a non-intrusive load monitoring | ||
| 653 | |a semi-automatic labeling | ||
| 653 | |a appliance load signatures | ||
| 653 | |a ambient influences | ||
| 653 | |a device classification accuracy | ||
| 653 | |a NILM | ||
| 653 | |a signature | ||
| 653 | |a load disaggregation | ||
| 653 | |a transients | ||
| 653 | |a pulse generator | ||
| 653 | |a smart metering | ||
| 653 | |a smart power grids | ||
| 653 | |a power consumption data | ||
| 653 | |a energy data processing | ||
| 653 | |a user-centric applications of energy data | ||
| 653 | |a convolutional neural network | ||
| 653 | |a energy consumption | ||
| 653 | |a energy data analytics | ||
| 653 | |a energy disaggregation | ||
| 653 | |a real-time | ||
| 653 | |a smart meter data | ||
| 653 | |a transient load signature | ||
| 653 | |a attention mechanism | ||
| 653 | |a deep neural network | ||
| 653 | |a electrical energy | ||
| 653 | |a load scheduling | ||
| 653 | |a satisfaction | ||
| 653 | |a Shapley Value | ||
| 653 | |a solar photovoltaics | ||
| 653 | |a review | ||
| 653 | |a deep learning | ||
| 653 | |a deep neural networks | ||
| 653 | |a n/a | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/4360 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76890 |7 0 |z DOAB: description of the publication |