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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 |
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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Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization
| Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization |
| Autore | Deschrijver Dirk |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (201 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
ant colony optimization
anti-icing appliance classification appliance feature big data process building energy consumption building load forecasting clustering CO2 reduction convolutional neural network design enclosure structure energy energy baselines energy consumption energy efficiency experimental validation field measurement forecasting fracturing roofs to maintain entry (FRME) fuel heat and mass transfer heat load reduction heat transfer coefficient heating power distribution machine learning manufacturing meta-heuristics modelling multi-objective combinatorial optimization n/a neural methods non-intrusive load monitoring numerical simulation optimization method passive house prediction predictive maintenance range recurrence graph regional side abutment pressure smart intelligent systems strata movement thermal improved of buildings turbo-propeller V-I trajectory weight weighted recurrence graph |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557346903321 |
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Deschrijver Dirk
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||