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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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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 online resource (206 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
artificial neural networks
biogas biogas plant biomethane BIPV window building performance business model chargeability factor critical success factor DC networks electric power system electrical energy energy management systems energy saving expert survey Ghana hospital building maintenance hospital buildings importance-performance matrix analysis information environment maintenance management minigrids multi-objective function n/a NCRES non-conventional renewable energy sources optimal power flow optimal set-points optimization optimization algorithms overall energy political support system power flow reactive power capacity renewable energy renewable energy sources RES rural electrification semi-arid stochastic optimization tilt angle value add value co-creation value-based practices visual comfort willingness to pay wind farm operation wind power generation wind speed and demand curves WWR |
| 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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