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Lademanagement für Elektrofahrzeuge

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ISBN: 9783731505655 Year: Pages: XIII, 209 DOI: 10.5445/KSP/1000057827 Language: GERMAN
Publisher: KIT Scientific Publishing
Subject: Business and Management
Added to DOAB on : 2019-07-30 20:02:02
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The present work describes a charging management with integrated charging optimization for electric vehicles that can be provided to a fleet by a fleet operator or to a group of private customers by a loading infrastructure operator. Aim of this charging optimization is the calculation of an optimized charging plan by considering the electricity price as well as hard capacity boundaries and user requirements. This charging optimization is designed and implemented within the scope of this work.

Hybrid Advanced Techniques for Forecasting in Energy Sector

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ISBN: 9783038972907 9783038972914 Year: Pages: 250 DOI: 10.3390/books978-3-03897-291-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-19 10:39:42
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Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

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.

Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

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ISBN: 9783038972921 9783038972938 Year: Pages: 186 DOI: 10.3390/books978-3-03897-293-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-22 10:01:53
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The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models. We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy.

Short-Term Load Forecasting by Artificial Intelligent Technologies

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ISBN: 9783038975823 / 9783038975830 Year: Pages: 444 DOI: 10.3390/books978-3-03897-583-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-01-29 10:55:39
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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

Algorithms for Scheduling Problems

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ISBN: 9783038971191 9783038971207 Year: Pages: XIV, 194 Language: Englisch
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- Mathematics
Added to DOAB on : 2018-08-24 16:46:30
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This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more.

Computational Intelligence in Photovoltaic Systems

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ISBN: 9783039210985 / 9783039210992 Year: Pages: 180 DOI: 10.3390/books978-3-03921-099-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 16:10:12
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Photovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems. Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems. This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields: Forecasting techniques (deterministic, stochastic, etc.); DC/AC converter control and maximum power point tracking techniques; Sizing and optimization of photovoltaic system components; Photovoltaics modeling and parameter estimation; Maintenance and reliability modeling; Decision processes for grid operators.

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

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ISBN: 9783039211425 / 9783039211432 Year: Pages: 394 DOI: 10.3390/books978-3-03921-143-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management.

Keywords

port scheduling --- berth-quay crane joint scheduling --- optimization study --- hybrid mathematical model --- multi-objective decision-making (MODM) --- sustainability --- vibration suppression --- single-cylinder engine --- multi-objective evolutionary algorithms --- dynamic analysis --- crank–slider --- ecological building --- clay blocks --- compacted clay --- straw bales --- cost calculation --- group decision making --- hesitant fuzzy set --- hospital evaluation --- linguistic hesitant fuzzy set and Standard variance --- bi-objective optimization --- heuristics --- discrete time/cost trade-off --- project scheduling --- Rough Hamy aggregator --- sustainable traffic --- Rough BWM --- Rough WASPAS --- construction --- roundabout --- optimization --- genetic algorithm --- artificial neural network --- apple --- drying --- rehydration --- renewable energy --- technology selection problem --- sustainable energy evaluation --- sustainable energy developments --- sustainable developments --- hierarchical SWARA --- MULTIMOORA --- multiple criteria decision making (MCDM) --- Multiple Attribute Decision Making (MADM) --- ranking --- healthcare facility --- location-allocation problem --- multiple objective optimization --- bi-level programming --- particle swarm optimization (PSO) --- cleaner production (CP) --- extended Tomada de Decisão Interativa Multicritério (TODIM) --- probabilistic linguistic term sets (PLTSs) --- hybrid multi-criteria decision making (MCDM) --- gold mines --- conceptual framework --- organizations --- sustainability --- sustainability hierarchy --- Total Interpretive Structural Modeling (TISM) --- sustainable transport --- public transport --- emission of pollutants --- travel times --- bus pass --- MCDM --- critical information infrastructures --- fuzzy --- AHP --- WSM --- WASPAS --- MCDM --- hybrid --- management --- grey --- SWARA --- TOPSIS-GM --- ARAS-G --- Geomean --- energy efficiency --- comfort of use of buildings --- historic buildings --- sustainable development --- surface transport --- innovation in transport --- policy measures --- sustainable transport policy --- multiple criteria decision aid --- hybrid expert system --- bat algorithm --- particle swarm optimization algorithm --- multi-purpose system --- water resource management --- project --- construction --- contractor --- multiple-criteria decision-making --- AHP --- sustainable solution --- choice --- expert --- building investment project --- risk --- assessment --- verbal analysis --- multiple-attribute decision-making (MADM) --- multi-objective decision-making (MODM) --- optimization --- engineering --- management --- sustainable development

Evolutionary Computation

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ISBN: 9783039219285 / 9783039219292 Year: Pages: 424 DOI: 10.3390/books978-3-03921-929-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,

Keywords

artificial bee colony algorithm (ABC) --- cloud model --- normal cloud model --- Y conditional cloud generator --- global optimum --- evolution --- computation --- urban design --- biology --- shape grammar --- architecture --- SPEA 2 --- energy-efficient job shop scheduling --- dispatching rule --- nonlinear convergence factor --- mutation operation --- whale optimization algorithm --- particle swarm optimization --- confidence term --- random weight --- benchmark functions --- t-test --- success rates --- average iteration times --- set-union knapsack problem --- moth search algorithm --- transfer function --- discrete algorithm --- evolutionary multi-objective optimization --- convergence point --- acceleration search --- evolutionary computation --- optimization --- bat algorithm (BA) --- bat algorithm with multiple strategy coupling (mixBA) --- CEC2013 benchmarks --- Wilcoxon test --- Friedman test --- facility layout design --- single loop --- monarch butterfly optimization --- slicing tree structure --- material handling path --- integrated design --- wireless sensor networks (WSNs) --- DV-Hop algorithm --- multi-objective DV-Hop localization algorithm --- NSGA-II-DV-Hop --- first-arrival picking --- fuzzy c-means --- particle swarm optimization --- range detection --- minimum total dominating set --- evolutionary algorithm --- genetic algorithm --- local search --- constrained optimization problems (COPs) --- evolutionary algorithms (EAs) --- firefly algorithm (FA) --- stochastic ranking (SR) --- Artificial bee colony --- swarm intelligence --- elite strategy --- dimension learning --- global optimization --- DE algorithm --- ?-Hilbert space --- topology structure --- quantum uncertainty property --- numerical simulation --- whale optimization algorithm --- flexible job shop scheduling problem --- nonlinear convergence factor --- adaptive weight --- variable neighborhood search --- elephant herding optimization --- EHO --- swarm intelligence --- individual updating strategy --- large-scale --- benchmark --- diversity maintenance --- particle swarm optimizer --- entropy --- large scale optimization --- minimum load coloring --- memetic algorithm --- evolutionary --- local search --- particle swarm optimization --- large-scale optimization --- adaptive multi-swarm --- diversity maintenance --- deep learning --- convolutional neural network --- rock types --- automatic identification --- monarch butterfly optimization --- greedy optimization algorithm --- global position updating operator --- 0-1 knapsack problems

Optimization Methods Applied to Power Systems: Volume 1

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ISBN: 9783039211302 / 9783039211319 Year: Pages: 382 DOI: 10.3390/books978-3-03921-131-9 Language: eng
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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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.

Keywords

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

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