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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| 格式: | Online |
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| 語言: | 英语 |
| 出版: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| 主題: | |
| 在線閱讀: | ONIX_20220111_9783036520162_625 |
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| _version_ | 1869514712683642880 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-76890 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-768902024-04-09T23:16:20Z Energy Data Analytics for Smart Meter Data Reinhardt, Andreas Pereira, Lucas smart grid nontechnical losses electricity theft detection synthetic minority oversampling technique K-means cluster random forest smart grids smart energy system smart meter GDPR data privacy ethics multi-label learning Non-intrusive Load Monitoring appliance recognition fryze power theory V-I trajectory Convolutional Neural Network distance similarity matrix activation current electric vehicle synthetic data exponential distribution Poisson distribution Gaussian mixture models mathematical modeling machine learning simulation Non-Intrusive Load Monitoring (NILM) NILM datasets power signature electric load simulation data-driven approaches smart meters text convolutional neural networks (TextCNN) time-series classification data annotation non-intrusive load monitoring semi-automatic labeling appliance load signatures ambient influences device classification accuracy NILM signature load disaggregation transients pulse generator smart metering smart power grids power consumption data energy data processing user-centric applications of energy data convolutional neural network energy consumption energy data analytics energy disaggregation real-time smart meter data transient load signature attention mechanism deep neural network electrical energy load scheduling satisfaction Shapley Value solar photovoltaics review deep learning deep neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues 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. 2022-01-11T13:45:21Z 2022-01-11T13:45:21Z 2021 book ONIX_20220111_9783036520162_625 9783036520162 9783036520179 https://directory.doabooks.org/handle/20.500.12854/76890 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4360 https://mdpi.com/books/pdfview/book/4360 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2017-9 10.3390/books978-3-0365-2017-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036520162 9783036520179 346 Basel, Switzerland open access |
| spellingShingle | smart grid nontechnical losses electricity theft detection synthetic minority oversampling technique K-means cluster random forest smart grids smart energy system smart meter GDPR data privacy ethics multi-label learning Non-intrusive Load Monitoring appliance recognition fryze power theory V-I trajectory Convolutional Neural Network distance similarity matrix activation current electric vehicle synthetic data exponential distribution Poisson distribution Gaussian mixture models mathematical modeling machine learning simulation Non-Intrusive Load Monitoring (NILM) NILM datasets power signature electric load simulation data-driven approaches smart meters text convolutional neural networks (TextCNN) time-series classification data annotation non-intrusive load monitoring semi-automatic labeling appliance load signatures ambient influences device classification accuracy NILM signature load disaggregation transients pulse generator smart metering smart power grids power consumption data energy data processing user-centric applications of energy data convolutional neural network energy consumption energy data analytics energy disaggregation real-time smart meter data transient load signature attention mechanism deep neural network electrical energy load scheduling satisfaction Shapley Value solar photovoltaics review deep learning deep neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Energy Data Analytics for Smart Meter Data |
| title | Energy Data Analytics for Smart Meter Data |
| title_full | Energy Data Analytics for Smart Meter Data |
| title_fullStr | Energy Data Analytics for Smart Meter Data |
| title_full_unstemmed | Energy Data Analytics for Smart Meter Data |
| title_short | Energy Data Analytics for Smart Meter Data |
| title_sort | energy data analytics for smart meter data |
| topic | smart grid nontechnical losses electricity theft detection synthetic minority oversampling technique K-means cluster random forest smart grids smart energy system smart meter GDPR data privacy ethics multi-label learning Non-intrusive Load Monitoring appliance recognition fryze power theory V-I trajectory Convolutional Neural Network distance similarity matrix activation current electric vehicle synthetic data exponential distribution Poisson distribution Gaussian mixture models mathematical modeling machine learning simulation Non-Intrusive Load Monitoring (NILM) NILM datasets power signature electric load simulation data-driven approaches smart meters text convolutional neural networks (TextCNN) time-series classification data annotation non-intrusive load monitoring semi-automatic labeling appliance load signatures ambient influences device classification accuracy NILM signature load disaggregation transients pulse generator smart metering smart power grids power consumption data energy data processing user-centric applications of energy data convolutional neural network energy consumption energy data analytics energy disaggregation real-time smart meter data transient load signature attention mechanism deep neural network electrical energy load scheduling satisfaction Shapley Value solar photovoltaics review deep learning deep neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | smart grid nontechnical losses electricity theft detection synthetic minority oversampling technique K-means cluster random forest smart grids smart energy system smart meter GDPR data privacy ethics multi-label learning Non-intrusive Load Monitoring appliance recognition fryze power theory V-I trajectory Convolutional Neural Network distance similarity matrix activation current electric vehicle synthetic data exponential distribution Poisson distribution Gaussian mixture models mathematical modeling machine learning simulation Non-Intrusive Load Monitoring (NILM) NILM datasets power signature electric load simulation data-driven approaches smart meters text convolutional neural networks (TextCNN) time-series classification data annotation non-intrusive load monitoring semi-automatic labeling appliance load signatures ambient influences device classification accuracy NILM signature load disaggregation transients pulse generator smart metering smart power grids power consumption data energy data processing user-centric applications of energy data convolutional neural network energy consumption energy data analytics energy disaggregation real-time smart meter data transient load signature attention mechanism deep neural network electrical energy load scheduling satisfaction Shapley Value solar photovoltaics review deep learning deep neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20220111_9783036520162_625 |