LEADER 04425nam 2200925z- 450 001 9910557603203321 005 20220111 035 $a(CKB)5400000000045371 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76778 035 $a(oapen)doab76778 035 $a(EXLCZ)995400000000045371 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aData-Intensive Computing in Smart Microgrids 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (238 p.) 311 08$a3-0365-1627-1 311 08$a3-0365-1628-X 330 $aMicrogrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area. 606 $aTechnology: general issues$2bicssc 610 $aAMI 610 $aautomatic generation control 610 $abattery energy storage systems 610 $abig data analytics 610 $acloud computing 610 $adata-intensive smart application 610 $adeep learning 610 $ademand response 610 $ademand response programs 610 $aelectricity consumption 610 $aelectricity load forecasting 610 $aelectricity theft detection 610 $aelectricity thefts 610 $aenergy management 610 $aenergy trade contract 610 $aExtreme Learning Machine 610 $afeature selection 610 $afog computing 610 $aGenetic Algorithm 610 $agreen community 610 $agreen data center 610 $aGrid Search 610 $aimbalanced data 610 $aintelligent control methods 610 $aload forecasting 610 $amicrogrid 610 $amulti-objective energy optimization 610 $an/a 610 $aNB-PLC 610 $aoptimization techniques 610 $aphotovoltaic 610 $aprocessing time 610 $areal time power management 610 $areal-time systems 610 $arenewable energy 610 $arenewable energy sources 610 $aresource allocation 610 $aresponse time 610 $ascheduling 610 $aSG 610 $asingle/multi-area power system 610 $asmart grid 610 $asmart grids 610 $asmart meter 610 $asoft computing control methods 610 $aSupport Vector Machine 610 $aTL 610 $avirtual inertial control 610 $awind 615 7$aTechnology: general issues 700 $aHerodotou$b Herodotos$4edt$01329476 702 $aHerodotou$b Herodotos$4oth 906 $aBOOK 912 $a9910557603203321 996 $aData-Intensive Computing in Smart Microgrids$93039485 997 $aUNINA