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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 issues --- electricity load forecasting --- smart grid --- feature selection --- Extreme Learning Machine --- Genetic Algorithm --- Support Vector Machine --- Grid Search --- AMI --- TL --- SG --- NB-PLC --- fog computing --- green community --- resource allocation --- processing time --- response time --- green data center --- microgrid --- renewable energy --- energy trade contract --- real time power management --- load forecasting --- optimization techniques --- deep learning --- big data analytics --- electricity theft detection --- smart grids --- electricity consumption --- electricity thefts --- smart meter --- imbalanced data --- data-intensive smart application --- cloud computing --- real-time systems --- multi-objective energy optimization --- renewable energy sources --- wind --- photovoltaic --- demand response programs --- energy management --- battery energy storage systems --- demand response --- scheduling --- automatic generation control --- single/multi-area power system --- intelligent control methods --- virtual inertial control --- soft computing control methods --- n/a

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The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

Technology: general issues --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- n/a