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The sustainability of the built environment can only be achieved through the maintenance planning of built facilities during their lifecycle while also considering social, economic, functional, technical, and ecological aspects. Stakeholders should be conscious of the existing tools and knowledge for the optimization of maintenance and rehabilitation actions in consideration of the degradation mechanisms and the risk of failure over time. Knowledge concerning the service life prediction of building elements is crucial to the definition, in a rational and technically informed way, of a set of maintenance strategies over the building’s life cycle. Service life prediction methodologies provide a better understanding of the degradation phenomenon of the analyzed elements, enabling the relation of the characteristics of these elements and their exposure, use, and maintenance conditions with their performance over time. This SI intends to provide an overview of the existing knowledge related to various aspects of “Life Cycle Prediction and Maintenance of Buildings”. Relevant topics to this Special Issue include: Methodologies for service life prediction of buildings and components; Maintainability of buildings and components; Serviceability of building elements; Maintenance and repair actions of buildings and components; Definition and optimization of maintenance policies; Financial analysis of various maintenance plans; Whole life cycle costing; Life cycle assessment.

History of engineering & technology --- energy efficiency --- indoor climate quality --- life cycle economy --- changing operational environment --- municipal building procurement --- climate targets --- Insurance --- mathematical models --- service life prediction models --- natural stone claddings --- insurance premium --- risk assessment --- linoleum and vinyl floorings --- inspection --- pathology --- statistical survey --- healthcare infrastructures --- technical condition --- performance characteristics --- degree of wear --- service life --- preventive maintenance --- Digitization --- Key Performance Indicators --- KPIs --- Asset Management --- Facility Management --- Operations Maintenance &amp --- Repairs --- Decision Support System --- Facility Condition Index --- building --- construction material --- life cycle costs --- thermal insulation system --- conservation --- natural stone --- long-term weathered --- water repellents --- durability --- single-sided NMR --- Life Cycle Assessment uncertainties --- seismic hazard --- building renovation --- retrofit --- buildings --- building components --- building elements --- climate change --- degradation --- maintainability --- service life prediction --- existing structures --- reinforced concrete --- time-dependent reliability --- life cycle --- Gaussian mixture models --- strength degradation --- steel corrosion --- secondary databases --- single-family house --- energy supply system --- payback period --- internal rate of return --- energy price --- Swedish climate zones --- climate adaptation --- maintenance --- operation --- management --- n/a

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The sustainability of the built environment can only be achieved through the maintenance planning of built facilities during their lifecycle while also considering social, economic, functional, technical, and ecological aspects. Stakeholders should be conscious of the existing tools and knowledge for the optimization of maintenance and rehabilitation actions in consideration of the degradation mechanisms and the risk of failure over time. Knowledge concerning the service life prediction of building elements is crucial to the definition, in a rational and technically informed way, of a set of maintenance strategies over the building’s life cycle. Service life prediction methodologies provide a better understanding of the degradation phenomenon of the analyzed elements, enabling the relation of the characteristics of these elements and their exposure, use, and maintenance conditions with their performance over time. This SI intends to provide an overview of the existing knowledge related to various aspects of “Life Cycle Prediction and Maintenance of Buildings”. Relevant topics to this Special Issue include: Methodologies for service life prediction of buildings and components; Maintainability of buildings and components; Serviceability of building elements; Maintenance and repair actions of buildings and components; Definition and optimization of maintenance policies; Financial analysis of various maintenance plans; Whole life cycle costing; Life cycle assessment.

History of engineering & technology --- energy efficiency --- indoor climate quality --- life cycle economy --- changing operational environment --- municipal building procurement --- climate targets --- Insurance --- mathematical models --- service life prediction models --- natural stone claddings --- insurance premium --- risk assessment --- linoleum and vinyl floorings --- inspection --- pathology --- statistical survey --- healthcare infrastructures --- technical condition --- performance characteristics --- degree of wear --- service life --- preventive maintenance --- Digitization --- Key Performance Indicators --- KPIs --- Asset Management --- Facility Management --- Operations Maintenance &amp --- Repairs --- Decision Support System --- Facility Condition Index --- building --- construction material --- life cycle costs --- thermal insulation system --- conservation --- natural stone --- long-term weathered --- water repellents --- durability --- single-sided NMR --- Life Cycle Assessment uncertainties --- seismic hazard --- building renovation --- retrofit --- buildings --- building components --- building elements --- climate change --- degradation --- maintainability --- service life prediction --- existing structures --- reinforced concrete --- time-dependent reliability --- life cycle --- Gaussian mixture models --- strength degradation --- steel corrosion --- secondary databases --- single-family house --- energy supply system --- payback period --- internal rate of return --- energy price --- Swedish climate zones --- climate adaptation --- maintenance --- operation --- management --- energy efficiency --- indoor climate quality --- life cycle economy --- changing operational environment --- municipal building procurement --- climate targets --- Insurance --- mathematical models --- service life prediction models --- natural stone claddings --- insurance premium --- risk assessment --- linoleum and vinyl floorings --- inspection --- pathology --- statistical survey --- healthcare infrastructures --- technical condition --- performance characteristics --- degree of wear --- service life --- preventive maintenance --- Digitization --- Key Performance Indicators --- KPIs --- Asset Management --- Facility Management --- Operations Maintenance &amp --- Repairs --- Decision Support System --- Facility Condition Index --- building --- construction material --- life cycle costs --- thermal insulation system --- conservation --- natural stone --- long-term weathered --- water repellents --- durability --- single-sided NMR --- Life Cycle Assessment uncertainties --- seismic hazard --- building renovation --- retrofit --- buildings --- building components --- building elements --- climate change --- degradation --- maintainability --- service life prediction --- existing structures --- reinforced concrete --- time-dependent reliability --- life cycle --- Gaussian mixture models --- strength degradation --- steel corrosion --- secondary databases --- single-family house --- energy supply system --- payback period --- internal rate of return --- energy price --- Swedish climate zones --- climate adaptation --- maintenance --- operation --- management

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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