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Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids
| Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids |
| Autore | Simões Marcelo Godoy |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (202 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
droop curve
frequency regulation fuzzy logic the rate of change of frequency reserve power smart grid energy Internet convolutional neural network decision optimization deep reinforcement learning electric load forecasting non-dominated sorting genetic algorithm II multi-layer perceptron adaptive neuro-fuzzy inference system meta-heuristic algorithms automatic generation control fuzzy neural network control thermostatically controlled loads back propagation algorithm particle swarm optimization load disaggregation artificial intelligence cognitive meters machine learning state machine NILM non-technical losses semi-supervised learning knowledge embed deep learning distribution network equipment condition assessment multi information source fuzzy iteration current balancing algorithm level-shifted SPWM medium-voltage applications multilevel current source inverter motor drives phase-shifted carrier SPWM STATCOM electricity forecasting CNN–LSTM very short-term forecasting (VSTF) short-term forecasting (STF) medium-term forecasting (MTF) long-term forecasting (LTF) asynchronous motor linear active disturbance rejection control error differentiation vector control renewable energy solar power plant Data Envelopment Analysis (DEA) Fuzzy Analytical Network Process (FANP) Fuzzy Theory |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557103703321 |
Simões Marcelo Godoy
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 electronic resource (258 p.) |
| Soggetto non controllato |
artificial neural network
home energy management systems conditional random fields LR ELR energy disaggregation artificial intelligence genetic algorithm decision tree static young’s modulus price scheduling self-adaptive differential evolution algorithm Marsh funnel energy yield point non-intrusive load monitoring mud rheology distributed genetic algorithm MCP39F511 Jetson TX2 sustainable development artificial neural networks transient signature load disaggregation smart villages ambient assisted living smart cities demand side management smart city CNN wireless sensor networks object detection drill-in fluid ERELM sandstone reservoirs RPN deep learning RELM smart grids multiple kernel learning load feature extraction NILM energy management energy efficient coverage insulator Faster R-CNN home energy management smart grid LSTM smart metering optimization algorithms forecasting plastic viscosity machine learning computational intelligence policy making support vector machine internet of things sensor network nonintrusive load monitoring demand response |
| ISBN | 3-03928-890-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404078103321 |
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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 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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