04425nam 2200925z- 450 991055760320332120220111(CKB)5400000000045371(oapen)https://directory.doabooks.org/handle/20.500.12854/76778(oapen)doab76778(EXLCZ)99540000000004537120202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierData-Intensive Computing in Smart MicrogridsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (238 p.)3-0365-1627-1 3-0365-1628-X Microgrids 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.Technology: general issuesbicsscAMIautomatic generation controlbattery energy storage systemsbig data analyticscloud computingdata-intensive smart applicationdeep learningdemand responsedemand response programselectricity consumptionelectricity load forecastingelectricity theft detectionelectricity theftsenergy managementenergy trade contractExtreme Learning Machinefeature selectionfog computingGenetic Algorithmgreen communitygreen data centerGrid Searchimbalanced dataintelligent control methodsload forecastingmicrogridmulti-objective energy optimizationn/aNB-PLCoptimization techniquesphotovoltaicprocessing timereal time power managementreal-time systemsrenewable energyrenewable energy sourcesresource allocationresponse timeschedulingSGsingle/multi-area power systemsmart gridsmart gridssmart metersoft computing control methodsSupport Vector MachineTLvirtual inertial controlwindTechnology: general issuesHerodotou Herodotosedt1329476Herodotou HerodotosothBOOK9910557603203321Data-Intensive Computing in Smart Microgrids3039485UNINA