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This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact.

History of engineering & technology --- demand response --- artificial neural network --- power predictions --- energy management --- genetic algorithm --- optimisation --- microgrid --- smart grid --- requests time --- cloud computing --- response time --- processing time --- resource allocation --- fog computing --- energy resource --- energy security --- energy sources --- Slovakia --- energy flexibility --- retrofitting interventions --- residential consumption --- electrification in the built environment --- smart cities --- smart energy management --- India --- energy efficiency --- low-carbon mobility --- water-energy nexus

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This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact.

History of engineering & technology --- demand response --- artificial neural network --- power predictions --- energy management --- genetic algorithm --- optimisation --- microgrid --- smart grid --- requests time --- cloud computing --- response time --- processing time --- resource allocation --- fog computing --- energy resource --- energy security --- energy sources --- Slovakia --- energy flexibility --- retrofitting interventions --- residential consumption --- electrification in the built environment --- smart cities --- smart energy management --- India --- energy efficiency --- low-carbon mobility --- water-energy nexus

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Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies.

artificial neural network --- downscaling --- innovative methods --- reservoir inflow forecasting --- simulation --- extreme events --- climate variability --- sparse monitoring network --- weighted mean analogue --- sampling errors --- precipitation --- drought indices --- discrete wavelet --- SWSI --- hyetograph --- trends --- climate change --- SIAP --- Kabul river basin --- Hurst exponent --- extreme rainfall --- evolutionary strategy --- the Cauca River --- hydrological drought --- global warming --- least square support vector regression --- polynomial normal transform --- TRMM --- satellite data --- Fiji --- heavy storm --- flood regime --- compound events --- random forest --- uncertainty --- seasonal climate forecast --- INDC pledge --- Pakistan --- wavelet artificial neural network --- HBV model --- temperature --- APCC Multi-Model Ensemble --- meteorological drought --- flow regime --- high resolution --- rainfall --- clausius-clapeyron scaling --- statistical downscaling --- ENSO --- forecasting --- variation analogue --- machine learning --- extreme rainfall analysis --- hydrological extremes --- multivariate modeling --- monsoon --- non-stationary --- support vector machine --- ANN model --- stretched Gaussian distribution --- drought prediction --- non-normality --- statistical analysis --- extreme precipitation exposure --- drought analysis --- extreme value theory --- streamflow --- flood management

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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance

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Very recently, a great deal of attention has been paid by researchers and technologists to trying to eliminate metal materials in the design of products and processes in favor of plastics and composites. After a few years, it is possible to state that metal materials are even more present in our lives and this is especially thanks to their ability to evolve. This Special Issue is focused on the recent evolution of metals and alloys with the scope of presenting the state of the art of solutions where metallic materials have become established, without a doubt, as a successful design solution thanks to their unique properties.

Technology: general issues --- material properties prediction --- experimental data analysis --- ductile/spheroidal cast iron (SGI) --- compact graphite cast iron (CGI) --- Machine Learning (RF) --- pattern recognition --- Random Forest (RF) --- Artificial Neural Network (NN) --- k-nearest neighbours (kNN) --- tribology --- wear --- slurry erosion --- coating --- cermet --- spheroidal graphite cast iron --- pack aluminizing --- microstructure --- high-temperature oxidation resistance --- hybrid composite --- wear performance --- ZA27 alloy --- deflection --- plates --- stiffeners --- numerical simulation --- Constructal Design --- austenitic stainless steel --- tensile properties --- artificial neural network --- MIV analysis --- pallet rack --- moment-rotation curve --- connection --- experiment --- numerical analysis --- thermomechanical processing --- grain growth --- forging --- retained austenite --- bainitic microstructure --- extended finite element method (xFEM) --- polarization curve --- long-term operated metals --- hybrid materials --- fatigue crack growth --- stress intensity factors (SIF) --- linear regression --- micromagnetic testing --- hardness --- case hardening depth --- phase-field modeling --- modified damage model --- large-strain plasticity --- S355J2+N steel --- ductile fracture --- two-stage yield function --- copper coatings --- pulsating current (PC) --- composite hardness models --- creep resistance