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Artificial Intelligence for Smart and Sustainable Energy Systems and Applications
| Artificial Intelligence for Smart and Sustainable Energy Systems and Applications |
| Autore | Lytras Miltiadis |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (258 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
ambient assisted living
artificial intelligence artificial neural network artificial neural networks CNN computational intelligence conditional random fields decision tree deep learning demand response demand side management distributed genetic algorithm drill-in fluid ELR energy energy disaggregation energy efficient coverage energy management ERELM Faster R-CNN feature extraction forecasting genetic algorithm home energy management home energy management systems insulator internet of things Jetson TX2 load load disaggregation LR LSTM machine learning Marsh funnel MCP39F511 mud rheology multiple kernel learning NILM non-intrusive load monitoring nonintrusive load monitoring object detection optimization algorithms plastic viscosity policy making price RELM RPN sandstone reservoirs scheduling self-adaptive differential evolution algorithm sensor network smart cities smart city smart grid smart grids smart metering smart villages static young's modulus support vector machine sustainable development transient signature wireless sensor networks yield point |
| ISBN | 3-03928-890-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404078103321 |
Lytras Miltiadis
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| 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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