Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

• Format: eBook
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: History of engineering and technology; ambient assisted living; artificial intelligence; artificial neural network; artificial neural networks; CNN; computational intelligence; conditional random fields; decision tree; deep learning; demand response; demand side management; distributed genetic algorithm; drill-in fluid; ELR; energy; energy disaggregation; energy efficient coverage; energy management; ERELM; Faster R-CNN; feature extraction; forecasting; genetic algorithm; home energy management; home energy management systems; insulator; internet of things; Jetson TX2; load; load disaggregation; LR; LSTM; machine learning; Marsh funnel; MCP39F511; mud rheology; multiple kernel learning; NILM; non-intrusive load monitoring; nonintrusive load monitoring; object detection; optimization algorithms; plastic viscosity; policy making; price; RELM; RPN; sandstone reservoirs; scheduling; self-adaptive differential evolution algorithm; sensor network; smart cities; smart city; smart grid; smart grids; smart metering; smart villages; static young's modulus; support vector machine; sustainable development; transient signature; wireless sensor networks; yield point
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (258 p.)
• ISBN: 9783039288892; 9783039288908; books978-3-03928-890-8
• Access: Open Access

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.