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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

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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 --- n/a

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In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.

Technology: general issues --- passive house --- enclosure structure --- heat transfer coefficient --- energy consumption --- turbo-propeller --- regional --- fuel --- weight --- range --- design --- CO2 reduction --- multi-objective combinatorial optimization --- meta-heuristics --- ant colony optimization --- non-intrusive load monitoring --- appliance classification --- appliance feature --- recurrence graph --- weighted recurrence graph --- V-I trajectory --- convolutional neural network --- energy baselines --- machine learning --- clustering --- neural methods --- smart intelligent systems --- building energy consumption --- building load forecasting --- energy efficiency --- thermal improved of buildings --- anti-icing --- heat and mass transfer --- heating power distribution --- heat load reduction --- optimization method --- experimental validation --- big data process --- predictive maintenance --- fracturing roofs to maintain entry (FRME) --- field measurement --- numerical simulation --- side abutment pressure --- strata movement --- energy --- manufacturing --- prediction --- forecasting --- modelling --- passive house --- enclosure structure --- heat transfer coefficient --- energy consumption --- turbo-propeller --- regional --- fuel --- weight --- range --- design --- CO2 reduction --- multi-objective combinatorial optimization --- meta-heuristics --- ant colony optimization --- non-intrusive load monitoring --- appliance classification --- appliance feature --- recurrence graph --- weighted recurrence graph --- V-I trajectory --- convolutional neural network --- energy baselines --- machine learning --- clustering --- neural methods --- smart intelligent systems --- building energy consumption --- building load forecasting --- energy efficiency --- thermal improved of buildings --- anti-icing --- heat and mass transfer --- heating power distribution --- heat load reduction --- optimization method --- experimental validation --- big data process --- predictive maintenance --- fracturing roofs to maintain entry (FRME) --- field measurement --- numerical simulation --- side abutment pressure --- strata movement --- energy --- manufacturing --- prediction --- forecasting --- modelling

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This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes.

Technology: general issues --- spatial-temporal data --- pasting process --- process image --- convolutional neural network --- Industry 4.0 --- auto machine learning --- failure mode effects analysis --- risk priority number --- rolling bearing --- condition monitoring --- classification --- OPTICS --- statistical process control --- control chart pattern --- disruptions --- disruption management --- fault diagnosis --- construction industry --- plaster production --- neural networks --- decision support systems --- expert systems --- failure mode and effects analysis (FMEA) --- discriminant analysis --- non-intrusive load monitoring --- load identification --- membrane --- data reconciliation --- real-time --- online --- monitoring --- Six Sigma --- multivariate data analysis --- latent variables models --- PCA --- PLS --- high-dimensional data --- statistical process monitoring --- artificial generation of variability --- data augmentation --- quality prediction --- continuous casting --- multiscale --- time series classification --- imbalanced data --- combustion --- optical sensors --- spectroscopy measurements --- signal detection --- digital processing --- principal component analysis --- curve resolution --- data mining --- semiconductor manufacturing --- quality control --- yield improvement --- fault detection --- process control --- multi-phase residual recursive model --- multi-mode model --- process monitoring --- n/a

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Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems.

microgrid --- distribution network operator --- double externalities --- subsidy --- PV system --- PI controller --- fuzzy control --- MPPT --- tracking speed --- error --- Micro Grid --- VSG --- power sharing --- inertia support --- energy support --- small signal stability --- day-ahead operational scheduling --- reconfigurable microgrid --- DRNN Bi-LSTM --- aggregated load forecasting --- bulk photovoltaic power generation forecasting --- solar potential assessment --- resource mapping --- geographic information systems (GIS) --- site selection --- Iran --- earthquake --- power distribution network --- resilience improvement planning --- water distribution network --- load disaggregation --- non-intrusive load monitoring (NILM) --- dimensionality reduction --- principal component analysis (PCA) --- smart home --- solar renewable --- thermal load --- stochastic operation --- energy storage --- sustainability --- desalination --- renewable energy --- water–energy-nexus --- photovoltaic grid-connected system --- power fluctuation --- DC bus voltage stabilization --- prescribed performance --- command-filtered adaptive backstepping control --- centralized control architecture --- DC microgrid --- distributed control architecture --- electricity price constraint --- hybrid control architecture --- power flow control strategy --- data pre-processing --- electricity theft --- imbalance data --- parameter tuning --- smart grid