LEADER 04179nam 2201093z- 450 001 9910404078103321 005 20210211 010 $a3-03928-890-3 035 $a(CKB)4100000011302357 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/41352 035 $a(oapen)doab41352 035 $a(EXLCZ)994100000011302357 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial Intelligence for Smart and Sustainable Energy Systems and Applications 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (258 p.) 311 08$a3-03928-889-X 330 $aEnergy 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. 606 $aHistory of engineering and technology$2bicssc 610 $aambient assisted living 610 $aartificial intelligence 610 $aartificial neural network 610 $aartificial neural networks 610 $aCNN 610 $acomputational intelligence 610 $aconditional random fields 610 $adecision tree 610 $adeep learning 610 $ademand response 610 $ademand side management 610 $adistributed genetic algorithm 610 $adrill-in fluid 610 $aELR 610 $aenergy 610 $aenergy disaggregation 610 $aenergy efficient coverage 610 $aenergy management 610 $aERELM 610 $aFaster R-CNN 610 $afeature extraction 610 $aforecasting 610 $agenetic algorithm 610 $ahome energy management 610 $ahome energy management systems 610 $ainsulator 610 $ainternet of things 610 $aJetson TX2 610 $aload 610 $aload disaggregation 610 $aLR 610 $aLSTM 610 $amachine learning 610 $aMarsh funnel 610 $aMCP39F511 610 $amud rheology 610 $amultiple kernel learning 610 $aNILM 610 $anon-intrusive load monitoring 610 $anonintrusive load monitoring 610 $aobject detection 610 $aoptimization algorithms 610 $aplastic viscosity 610 $apolicy making 610 $aprice 610 $aRELM 610 $aRPN 610 $asandstone reservoirs 610 $ascheduling 610 $aself-adaptive differential evolution algorithm 610 $asensor network 610 $asmart cities 610 $asmart city 610 $asmart grid 610 $asmart grids 610 $asmart metering 610 $asmart villages 610 $astatic young's modulus 610 $asupport vector machine 610 $asustainable development 610 $atransient signature 610 $awireless sensor networks 610 $ayield point 615 7$aHistory of engineering and technology 700 $aLytras$b Miltiadis$4auth$01149833 702 $aChui$b Kwok Tai$4auth 906 $aBOOK 912 $a9910404078103321 996 $aArtificial Intelligence for Smart and Sustainable Energy Systems and Applications$93035777 997 $aUNINA