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Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

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 researc...

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Main Authors: Lytras, Miltiadis, Chui, Kwok Tai
Format: Online
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
TA1-2040
T1-995
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
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online Access:46125
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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.

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