Lytras, Miltiadis <br/><br/>
Artificial Intelligence for Smart and Sustainable Energy Systems and Applications 

 -  MDPI - Multidisciplinary Digital Publishing Institute  2020 
 - 1 electronic resource (258 p.) 
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><br/>Creative Commons 
<br/><br/><br/>English 
<br/><br/><label>ISBN: </label>9783039288892 9783039288908 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Искусственный интеллект
<br/><br/><label>Subjects--Index Terms: </label>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 
<br/><br/><label>Universal Decimal Class. No.: </label>004.8