Choose an application

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

Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.

short-term load forecasting --- demand-side management --- pattern similarity --- hierarchical short-term load forecasting --- feature selection --- weather station selection --- load forecasting --- special days --- regressive models --- electric load forecasting --- data preprocessing technique --- multiobjective optimization algorithm --- combined model --- Nordic electricity market --- electricity demand --- component estimation method --- univariate and multivariate time series analysis --- modeling and forecasting --- deep learning --- wavenet --- long short-term memory --- demand response --- hybrid energy system --- data augmentation --- convolution neural network --- residential load forecasting --- forecasting --- time series --- cubic splines --- real-time electricity load --- seasonal patterns --- Load forecasting --- VSTLF --- bus load forecasting --- DBN --- PSR --- distributed energy resources --- prosumers --- building electric energy consumption forecasting --- cold-start problem --- transfer learning --- multivariate random forests --- random forest --- electricity consumption --- lasso --- Tikhonov regularization --- load metering --- preliminary load --- short term load forecasting --- performance criteria --- power systems --- cost analysis --- day ahead --- feature extraction --- deep residual neural network --- multiple sources --- electricity

Choose an application

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

Smart cities operate under more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which led to the creation of smart enterprises and organizations that depend on advanced technologies. This book includes 21 selected and peer-reviewed articles contributed in the wide spectrum of artificial intelligence applications to smart cities. Chapters refer to the following areas of interest: vehicular traffic prediction, social big data analysis, smart city management, driving and routing, localization, safety, health, and life quality.

Information technology industries --- spatio-temporal --- residual networks --- bus traffic flow prediction --- advance rate --- shield performance --- principal component analysis --- ANFIS-GA --- tunnel --- online learning --- extreme learning machine --- cyclic dynamics --- transfer learning --- knowledge preservation --- Feature Adaptive --- optimization --- Bacterial Foraging algorithm --- Swarm Intelligence algorithm --- Isolated Microgrid --- traffic surveillance video --- state analysis --- Grassmann manifold --- neural network --- machine-learning --- quality of life --- Better Life Index --- bagging --- ensemble learning --- pedestrian attributes --- surveillance image --- semantic attributes recognition --- multi-label learning --- large-scale database --- traffic congestion detection --- minimizing traffic congestion --- traffic prediction --- deep learning --- urban mobility --- ITS --- Vehicle-to-Infrastructure --- neural networks --- LSTM --- embeddings --- trajectories --- motion behavior --- smart tourism --- driver’s behavior detection --- texting and driving --- convolutional neural network --- smart car --- smart cities --- smart infotainment --- driver distraction --- cameras --- convolution --- detection --- image recognition --- DSS --- diabetes prediction --- homecare assistance information system --- muti-attribute analysis --- artificial training dataset --- machine learning --- big data --- data analysis --- sensors --- Internet of Things --- vehicular networks --- VDTN --- routing --- message scheduling --- traffic flow prediction --- wavenet --- TrafficWave --- RNN --- GRU --- SAEs --- risk assessment --- neural architecture search --- recurrent neural network --- automated driving vehicle --- decision support system --- artificial intelligence --- disaster management --- Smart city --- program management --- integrated model --- smart city --- intelligence transportation system --- computer vision --- potential pedestrian safety --- data mining --- healthcare --- Apache Spark --- disease detection --- symptoms detection --- Arabic language --- Saudi dialect --- Twitter --- high performance computing (HPC) --- spatial-temporal dependencies --- traffic periodicity --- graph convolutional network --- traffic speed prediction --- vehicular traffic --- surveillance video --- big data analysis --- autonomous driving --- life quality --- pattern recognition

Choose an application

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

Smart cities operate under more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which led to the creation of smart enterprises and organizations that depend on advanced technologies. This book includes 21 selected and peer-reviewed articles contributed in the wide spectrum of artificial intelligence applications to smart cities. Chapters refer to the following areas of interest: vehicular traffic prediction, social big data analysis, smart city management, driving and routing, localization, safety, health, and life quality.

Information technology industries --- spatio-temporal --- residual networks --- bus traffic flow prediction --- advance rate --- shield performance --- principal component analysis --- ANFIS-GA --- tunnel --- online learning --- extreme learning machine --- cyclic dynamics --- transfer learning --- knowledge preservation --- Feature Adaptive --- optimization --- Bacterial Foraging algorithm --- Swarm Intelligence algorithm --- Isolated Microgrid --- traffic surveillance video --- state analysis --- Grassmann manifold --- neural network --- machine-learning --- quality of life --- Better Life Index --- bagging --- ensemble learning --- pedestrian attributes --- surveillance image --- semantic attributes recognition --- multi-label learning --- large-scale database --- traffic congestion detection --- minimizing traffic congestion --- traffic prediction --- deep learning --- urban mobility --- ITS --- Vehicle-to-Infrastructure --- neural networks --- LSTM --- embeddings --- trajectories --- motion behavior --- smart tourism --- driver’s behavior detection --- texting and driving --- convolutional neural network --- smart car --- smart cities --- smart infotainment --- driver distraction --- cameras --- convolution --- detection --- image recognition --- DSS --- diabetes prediction --- homecare assistance information system --- muti-attribute analysis --- artificial training dataset --- machine learning --- big data --- data analysis --- sensors --- Internet of Things --- vehicular networks --- VDTN --- routing --- message scheduling --- traffic flow prediction --- wavenet --- TrafficWave --- RNN --- GRU --- SAEs --- risk assessment --- neural architecture search --- recurrent neural network --- automated driving vehicle --- decision support system --- artificial intelligence --- disaster management --- Smart city --- program management --- integrated model --- smart city --- intelligence transportation system --- computer vision --- potential pedestrian safety --- data mining --- healthcare --- Apache Spark --- disease detection --- symptoms detection --- Arabic language --- Saudi dialect --- Twitter --- high performance computing (HPC) --- spatial-temporal dependencies --- traffic periodicity --- graph convolutional network --- traffic speed prediction --- vehicular traffic --- surveillance video --- big data analysis --- autonomous driving --- life quality --- pattern recognition