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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 electronic resource (238 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
electricity load forecasting
smart grid feature selection Extreme Learning Machine Genetic Algorithm Support Vector Machine Grid Search AMI TL SG NB-PLC fog computing green community resource allocation processing time response time green data center microgrid renewable energy energy trade contract real time power management load forecasting optimization techniques deep learning big data analytics electricity theft detection smart grids electricity consumption electricity thefts smart meter imbalanced data data-intensive smart application cloud computing real-time systems multi-objective energy optimization renewable energy sources wind photovoltaic demand response programs energy management battery energy storage systems demand response scheduling automatic generation control single/multi-area power system intelligent control methods virtual inertial control soft computing control methods |
| 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 electronic resource (346 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
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 |
| 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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