Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Lytras, Miltiadis; Chui, Kwok Tai

doi:10.3390/books978-3-03928-890-8

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https://mdpi.com/books/pdfview/book/2319

Author(s)

Lytras, Miltiadis

Chui, Kwok Tai

Language

English

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Abstract

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.

URI

https://directory.doabooks.org/handle/20.500.12854/41352

Keywords

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