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dc.contributor.editorHerodotou, Herodotos
dc.description.abstractMicrogrids 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.
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.otherelectricity load forecasting
dc.subject.othersmart grid
dc.subject.otherfeature selection
dc.subject.otherExtreme Learning Machine
dc.subject.otherGenetic Algorithm
dc.subject.otherSupport Vector Machine
dc.subject.otherGrid Search
dc.subject.otherfog computing
dc.subject.othergreen community
dc.subject.otherresource allocation
dc.subject.otherprocessing time
dc.subject.otherresponse time
dc.subject.othergreen data center
dc.subject.otherrenewable energy
dc.subject.otherenergy trade contract
dc.subject.otherreal time power management
dc.subject.otherload forecasting
dc.subject.otheroptimization techniques
dc.subject.otherdeep learning
dc.subject.otherbig data analytics
dc.subject.otherelectricity theft detection
dc.subject.othersmart grids
dc.subject.otherelectricity consumption
dc.subject.otherelectricity thefts
dc.subject.othersmart meter
dc.subject.otherimbalanced data
dc.subject.otherdata-intensive smart application
dc.subject.othercloud computing
dc.subject.otherreal-time systems
dc.subject.othermulti-objective energy optimization
dc.subject.otherrenewable energy sources
dc.subject.otherdemand response programs
dc.subject.otherenergy management
dc.subject.otherbattery energy storage systems
dc.subject.otherdemand response
dc.subject.otherautomatic generation control
dc.subject.othersingle/multi-area power system
dc.subject.otherintelligent control methods
dc.subject.othervirtual inertial control
dc.subject.othersoft computing control methods
dc.titleData-Intensive Computing in Smart Microgrids
oapen.pages238, Switzerland

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