Energy Data Analytics for Smart Meter Data

• Autore: Reinhardt Andreas
• Pubblicazione: 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
• Persona (resp. second.): PereiraLucas; ReinhardtAndreas
• Sommario/riassunto: The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.
• Titolo autorizzato: Energy Data Analytics for Smart Meter Data
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557645803321
• Lo trovi qui: Univ. Federico II