04046nam 2201057z- 450 991040407810332120240301154816.03-03928-890-3(CKB)4100000011302357(oapen)https://directory.doabooks.org/handle/20.500.12854/41352(EXLCZ)99410000001130235720202102d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Smart and Sustainable Energy Systems and ApplicationsMDPI - Multidisciplinary Digital Publishing Institute20201 electronic resource (258 p.)3-03928-889-X 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.artificial neural networkhome energy management systemsconditional random fieldsLRELRenergy disaggregationartificial intelligencegenetic algorithmdecision treestatic young’s moduluspriceschedulingself-adaptive differential evolution algorithmMarsh funnelenergyyield pointnon-intrusive load monitoringmud rheologydistributed genetic algorithmMCP39F511Jetson TX2sustainable developmentartificial neural networkstransient signatureload disaggregationsmart villagesambient assisted livingsmart citiesdemand side managementsmart cityCNNwireless sensor networksobject detectiondrill-in fluidERELMsandstone reservoirsRPNdeep learningRELMsmart gridsmultiple kernel learningloadfeature extractionNILMenergy managementenergy efficient coverageinsulatorFaster R-CNNhome energy managementsmart gridLSTMsmart meteringoptimization algorithmsforecastingplastic viscositymachine learningcomputational intelligencepolicy makingsupport vector machineinternet of thingssensor networknonintrusive load monitoringdemand responseLytras Miltiadisauth1149833Chui Kwok TaiauthBOOK9910404078103321Artificial Intelligence for Smart and Sustainable Energy Systems and Applications3035777UNINA