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Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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ISBN: 9783038972860 9783038972877 Year: Pages: 250 DOI: 10.3390/books978-3-03897-287-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2018-10-19 11:45:03
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More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.

Intelligent Optimization Modelling in Energy Forecasting

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ISBN: 9783039283644 9783039283651 Year: Pages: 262 DOI: 10.3390/books978-3-03928-365-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2020-04-07 23:07:09
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Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Keywords

short-term load forecasting --- weighted k-nearest neighbor (W-K-NN) algorithm --- comparative analysis --- empirical mode decomposition (EMD) --- particle swarm optimization (PSO) algorithm --- intrinsic mode function (IMF) --- support vector regression (SVR) --- short term load forecasting --- crude oil price forecasting --- time series forecasting --- hybrid model --- complementary ensemble empirical mode decomposition (CEEMD) --- sparse Bayesian learning (SBL) --- multi-step wind speed prediction --- Ensemble Empirical Mode Decomposition --- Long Short Term Memory --- General Regression Neural Network --- Brain Storm Optimization --- substation project cost forecasting model --- feature selection --- data inconsistency rate --- modified fruit fly optimization algorithm --- deep convolutional neural network --- multi-objective grey wolf optimizer --- long short-term memory --- fuzzy time series --- LEM2 --- combination forecasting --- wind speed --- electrical power load --- crude oil prices --- time series forecasting --- improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) --- kernel learning --- kernel ridge regression --- differential evolution (DE) --- artificial intelligence techniques --- energy forecasting --- condition-based maintenance --- asset management --- renewable energy consumption --- Gaussian processes regression --- state transition algorithm --- five-year project --- forecasting --- Markov-switching --- Markov-switching GARCH --- energy futures --- commodities --- portfolio management --- active investment --- diversification --- institutional investors --- energy price hedging --- metamodel --- ensemble --- individual --- regression --- interpolation

Adaptive Catchment Management and Reservoir Operation

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ISBN: 9783038977384 9783038977391 Year: Pages: 498 DOI: 10.3390/books978-3-03897-739-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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River catchments and reservoirs play a central role in water security, food supply, flood risk management, hydropower generation, and ecosystem services; however, they are now under increasing pressure from population growth, economic activities, and changing climate means and extremes in many parts of the world. Adaptive management of river catchments and reservoirs requires an in-depth understanding of the impacts of future uncertainties and thus the development of robust, sustainable solutions to meet the needs of various stakeholders and the environment. To tackle the huge challenges in moving towards adaptive catchment management, this book presents the latest developments in cutting-edge knowledge, novel methodologies, innovative management strategies, and case studies, focusing on the following themes: reservoir dynamics and impact analysis of dam construction, optimal reservoir operation, climate change impacts on hydrological processes and water management, and integrated catchment management.

Keywords

Siemianówka --- hydrology --- Narew River --- dam --- reservoir --- discharge --- flow regime --- reservoir flushing --- numerical simulation --- flushing efficiency --- Kurobe River --- two-dimensional bed evolution model --- sediment flushing of empty storage --- shaft spillway pipe --- sediment flushing efficiency --- sediment regime --- suspended sediment concentration --- vertical profiles of concentration --- Jingjiang River Reach --- Yangtze River --- CO2 --- reservoirs --- general regression neural network --- back propagation neural network --- climate change --- CMIP3 --- CMIP5 --- downscaling --- runoff response --- SWAT model --- stochastic linear programming --- Markov chain --- reliability --- vulnerability --- reservoir operation --- stochastic dynamic programming --- protection zone --- nutrient uptake --- NPP --- South-to-North Water Transfer Project --- Miyun Reservoir --- reservoir operation --- optimization --- SWAT --- HEC-ResPRM --- climate change --- CORDEX-Africa --- Tekeze basin --- long distance water diversion --- inverted siphon --- sensitivity analysis --- integrated supply system modeling --- sediment regime --- suspended sediment concentration --- vertical profiles of concentration --- the Jingjiang River Reach --- the Yangtze River --- reservoir operation --- multi-stage stochastic optimization --- TB-MPC --- flood control --- real-time control --- energy --- hydropower stations --- differential evolution algorithm --- optimal scheduling --- ?-constrained method --- drinking water resources --- water environmental capacity (WEC) --- Environmental Fluid Dynamics Code (EFDC) model --- the Huangshi Reservoir --- seasonal rainfall --- upper Chao Phraya River Basin --- El Niño/Southern Oscillation --- Indian Monsoon --- sea surface temperatures --- reverse regulation --- coupling model --- aftereffect --- accompanying progressive optimality algorithm --- Dokan Dam --- runoff --- sediment load --- SWAT --- natural flow regime --- multi-objective model --- uncertainty --- genetic algorithm --- land and water resources --- system dynamics --- modeling --- scenario analysis --- Heilongjiang --- tropical reservoir --- heating impact --- Langcang-Mekong River --- Kappa distribution --- parameter relation --- partial gauged basin --- power function --- ratio curve --- ungauged basin --- reservoir operation --- integrated surface water-groundwater model --- Heihe River Basin --- environmental flow --- irrigation --- design and operation of the multipurpose reservoir --- water deficit --- reservoir simulation model --- climate change --- multi-objective optimization NSGA II --- resilience and robustness --- costs and benefits --- water energy --- multi-agent of river basin --- game theory --- water resources allocation --- optimal flood control operation --- cascade reservoirs --- dynamic programming with progressive optimality algorithm (DP-POA) --- the upper Yangtze River Basin --- parameterization --- simulation --- optimization --- direct policy search --- hedging policy --- shortage ratio: Vulnerability --- NSGA-II --- lentic habitats --- bitterling --- mussel --- floodplain vertical shape index --- sediment management --- adaptive management --- catchment modelling --- integrated management --- reservoir operation

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