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Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.

polyacrylonitrile-based carbon fiber --- n/a --- coagulation bath --- binder dissolution --- sensitivity analysis --- simulation --- neural networks --- kernel development --- thermodynamics --- phytochemicals --- wave resonance --- natural extracts --- population balance model --- optimization --- vane --- parameter estimation --- grey-box model --- observability --- optimal clustering --- energy --- idling test --- data-mining --- extents --- computational fluid dynamics --- scrap dissolution --- Combined Heat and Power --- dynamic optimization --- scrap melting --- swelling --- engineering --- dry-jet wet spinning process --- fluid bed granulation --- point estimation method --- algebraic modeling language --- Design of Experiments --- costing stopping --- materials --- hydration --- SOS programming --- kinetics --- moisture content --- CHP legislation --- model predictive control --- graph theory --- robust optimization --- dynamic converter modelling --- partial least square regression --- uncertainty --- state decoupling --- utility management --- fluidized bed drying --- reactor coolant pump --- condensation --- wheat germ --- cooking --- maximum wave amplitude --- moving horizon estimation --- gray-box model --- chemistry --- barley --- machine learning --- heat and mass balance --- equality constraints --- porridge --- process model validation --- Pharmaceutical Processes --- mathematical model --- model identification --- Mammalian Cell Culture --- process modeling --- parameter correlation

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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.

History of engineering & technology --- 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

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This paper uses the night lights (satellite imagery from outer space) approach to estimate growth in and levels of subnational 2013 gross domestic product for 47 counties in Kenya and 30 districts in Rwanda. Estimating subnational gross domestic product is consequential for three reasons. First, there is strong policy interest in how growth can occur in different parts of countries, so that communities can share in national prosperity and not get left behind. Second, subnational entities want to understand how they stack up against their neighbors and competitors, and how much they contribute to national gross domestic product. Third, such information could help private investors to assess where to undertake investments. Using night lights has the advantage of seeing a new and more accurate estimation of informal activity, and being independent of official data. However, the approach may underestimate economic activity in sectors that are largely unlit notably agriculture. For Kenya, the results of the analysis affirm that Nairobi County is the largest contributor to national gross domestic product. However, at 13 percent, this contribution is lower than commonly thought. For Rwanda, the three districts of Kigali account for 40 percent of national gross domestic product, underscoring the lower scale of economic activity in the rest of the country. To get a composite picture of subnational economic activity, especially in the context of rapidly improving official statistics in Kenya and Rwanda, it is important to estimate subnational gross domestic product using standard approaches (production, expenditure, income).

Agricultural output --- Agricultural performance --- Agricultural sector --- Agriculture --- Annual growth --- Annual growth rate --- Cities --- City --- Coefficients --- Consumption --- Criteria --- Development indicators --- Development policy --- Diseconomies of scale --- Distribution of income --- District --- District administrations --- District level --- District-level --- Economic activity --- Economic decline --- Economic downturns --- Economic growth --- Economic theory & research --- Economics --- Elasticity --- Empirical model --- Estimation method --- Financial crisis --- Fiscal management --- Fixed effects --- GDP --- GDP per capita --- Gross domestic product --- Growth --- Growth rate --- Growth rates --- Household surveys --- Incentives --- Incidence of poverty --- Indicators --- Informal economy --- Inputs --- Long-term growth --- Macroeconomics --- Macroeconomics and economic growth --- National poverty line --- Policy research --- Poverty --- Poverty impact evaluation --- Poverty levels --- Poverty line --- Poverty reduction --- Pro-poor growth --- Provinces --- Real GDP --- Resource allocation --- Revenue --- Revenue allocation --- Revenue sharing --- Revenue sharing formula --- Revenue-raising capacity --- Subnational entities --- Subnational governments --- Subnational unit --- Surveys --- Tax --- Underestimates --- Urban areas --- Wealth

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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.

History of engineering & technology --- 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 --- 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

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This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and Mixed-Integer Programming to the most modern methods based on bio-inspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.

n/a --- Stackelberg game --- MILP --- optimal congestion threshold --- magnetic field mitigation --- simulation --- multi-objective particle swarm optimization --- virtual power plant --- internal defect --- day-ahead load forecasting --- neural network --- modular predictor --- multi-objective particle swarm optimization algorithm --- stochastic optimization --- dragonfly algorithm --- unit commitment --- metaheuristic --- multi-population method (MP) --- optimization --- tabu search --- considerable decomposition --- loss minimization --- active distribution system --- islanded microgrid --- dynamic solving framework --- feature selection --- electric energy costs --- power factor compensation --- dependability --- interactive load --- overhead --- energy internet --- evolutionary computation --- wind power --- developed grew wolf optimizer --- underground --- ETAP --- fuzzy algorithm --- electric vehicles --- Schwarz’s equation --- evolutionary algorithms --- electric power contracts --- congestion management --- optimizing-scenarios method --- building energy management system --- particle encoding method --- ringdown detection --- HOMER software --- DC optimal power flow --- prosumer --- constrained parameter estimation --- distributed generations (DGs) --- strong track filter (STF) --- transient stability --- calibration --- cost minimization --- radiance --- decentralized and collaborative optimization --- hybrid renewable energy system --- renewable energy sources --- rural electrification --- distribution network reconfiguration --- interval variables --- optimization methods --- particle swarm optimization --- hierarchical scheduling --- micro grid --- AC/DC hybrid active distribution --- consensus --- artificial bee colony --- CCHP system --- data center --- support vector machine --- affinity propagation clustering --- extended Kalman filter --- affine arithmetic --- linear discriminant analysis (LDA) --- current margins --- heterogeneous networks --- Cameroon --- hybrid method --- distributed heat-electricity energy management --- discrete wind driven optimization --- fitness function --- cross-entropy --- GenOpt --- wind energy --- demand uncertainty --- UC --- off-design performance --- genetic algorithm --- energy storage --- the biomimetic membrane computing --- power system optimization --- electric vehicle --- power architectures --- economic load dispatch problem (ELD) --- runner-root algorithm (RRA) --- Cable joint --- battery energy storage system --- load curtailment --- integration assessment --- power system unit commitment --- artificial lighting --- power flow --- hybrid membrane computing --- two-point estimation method --- low-voltage networks --- demand bidding --- non-sinusoidal circuits --- energy flow model --- power transfer distribution factors --- sustainability --- HVAC system --- voltage deviation --- street light points --- radial basis function --- energy storage system --- charging/discharging --- power systems --- intelligent scatter search --- MV/LV substation --- optimal power flow --- stochastic state estimation --- eight searching sub-regions --- chaos optimization algorithm (COA) --- mutual information theory --- inter-turn shorted-circuit fault (ISCF) --- C&I particle swarm optimization --- multiobjective optimization --- passive shielding --- sub-Saharan Africa --- micro-phasor measurement unit --- geometric algebra --- bio-inspired algorithms --- adaptive consensus algorithm --- energy management --- PCS efficiency --- multi-stakeholders --- generalized generation distribution factors --- the genetic algorithm based P system --- JAYA algorithm --- thermal probability density --- power optimization --- pumped-hydro energy storage --- smart grid --- two-stage feature selection --- piecewise linear techniques --- photovoltaic --- SOCP relaxations --- switched reluctance machine (SRM) --- optimal reactive power dispatch --- optimal operation --- controllable response --- off-grid --- active shielding --- transformer-fault diagnosis --- IEEE Std. 80-2000 --- principal component analysis --- demand response --- Schwarz's equation