Reprint

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Edited by
May 2020
258 pages
  • ISBN978-3-03928-889-2 (Paperback)
  • ISBN978-3-03928-890-8 (PDF)

This book is a reprint of the Special Issue Artificial Intelligence for Smart and Sustainable Energy Systems and Applications that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.
Format
  • Paperback
License
© 2020 by the authors; CC BY licence
Keywords
artificial intelligence; demand response; energy; policy making; genetic algorithm; multiple kernel learning; non-intrusive load monitoring; smart grid; smart metering; support vector machine; smart cities; smart villages; scheduling; demand side management; smart grid; home energy management; NILM; energy disaggregation; MCP39F511; Jetson TX2; transient signature; decision tree; LSTM; wireless sensor networks; energy efficient coverage; distributed genetic algorithm; smart grid; forecasting; load; price; CNN; LR; ELR; RELM; ERELM; insulator; Faster R-CNN; object detection; RPN; deep learning; load disaggregation; nonintrusive load monitoring; conditional random fields; feature extraction; mud rheology; drill-in fluid; artificial neural network; Marsh funnel; plastic viscosity; yield point; static young’s modulus; artificial neural networks; self-adaptive differential evolution algorithm; sandstone reservoirs; non-intrusive load monitoring; home energy management systems; ambient assisted living; demand response; machine learning; internet of things; smart grids; artificial intelligence; computational intelligence; energy management; machine learning; optimization algorithms; sensor network; smart city; smart grid; sustainable development