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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 |
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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Renewable Energy and Energy Saving: Worldwide Research Trends
| Renewable Energy and Energy Saving: Worldwide Research Trends |
| Autore | Perea-Moreno Alberto-Jesus |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (206 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
BIPV window
WWR overall energy tilt angle visual comfort energy saving semi-arid wind power generation artificial neural networks chargeability factor reactive power capacity wind speed and demand curves energy management systems multi-objective function optimal set-points stochastic optimization wind farm operation expert survey renewable energy biogas biomethane biogas plant business model political support system building performance value co-creation value add maintenance management hospital buildings optimal power flow power flow optimization algorithms DC networks electrical energy optimization willingness to pay minigrids rural electrification Ghana hospital building maintenance critical success factor value-based practices importance-performance matrix analysis renewable energy sources non-conventional renewable energy sources RES NCRES electric power system information environment |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Renewable Energy and Energy Saving |
| Record Nr. | UNINA-9910585942803321 |
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Perea-Moreno Alberto-Jesus
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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