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Data-Intensive Computing in Smart Microgrids
| Data-Intensive Computing in Smart Microgrids |
| Autore | Herodotou Herodotos |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (238 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
AMI
automatic generation control battery energy storage systems big data analytics cloud computing data-intensive smart application deep learning demand response demand response programs electricity consumption electricity load forecasting electricity theft detection electricity thefts energy management energy trade contract Extreme Learning Machine feature selection fog computing Genetic Algorithm green community green data center Grid Search imbalanced data intelligent control methods load forecasting microgrid multi-objective energy optimization n/a NB-PLC optimization techniques photovoltaic processing time real time power management real-time systems renewable energy renewable energy sources resource allocation response time scheduling SG single/multi-area power system smart grid smart grids smart meter soft computing control methods Support Vector Machine TL virtual inertial control wind |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557603203321 |
Herodotou Herodotos
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Energy Data Analytics for Smart Meter Data
| Energy Data Analytics for Smart Meter Data |
| Autore | Reinhardt Andreas |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (346 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
activation current
ambient influences appliance load signatures appliance recognition attention mechanism convolutional neural network Convolutional Neural Network data annotation data privacy data-driven approaches deep learning deep neural network deep neural networks device classification accuracy distance similarity matrix electric load simulation electric vehicle electrical energy electricity theft detection energy consumption energy data analytics energy data processing energy disaggregation ethics exponential distribution fryze power theory Gaussian mixture models GDPR K-means cluster load disaggregation load scheduling machine learning mathematical modeling multi-label learning n/a NILM NILM datasets non-intrusive load monitoring Non-intrusive Load Monitoring Non-Intrusive Load Monitoring (NILM) nontechnical losses Poisson distribution power consumption data power signature pulse generator random forest real-time review satisfaction semi-automatic labeling Shapley Value signature simulation smart energy system smart grid smart grids smart meter smart meter data smart metering smart meters smart power grids solar photovoltaics synthetic data synthetic minority oversampling technique text convolutional neural networks (TextCNN) time-series classification transient load signature transients user-centric applications of energy data V-I trajectory |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557645803321 |
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Reinhardt Andreas
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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