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Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

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

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Información

Tipo de recurso:

libros

ISBN electrónico

978-3-03928-890-8

País de edición

Suiza