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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.

Continuous Casting

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ISBN: 9783039213214 / 9783039213221 Year: Pages: 250 DOI: 10.3390/books978-3-03921-322-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Chemical Engineering
Added to DOAB on : 2019-08-28 11:21:27
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Continuous casting is an industrial process whereby molten metal is solidified into a semi-finished billet, bloom, or slab for subsequent rolling in finishing mills; it is the most frequently used process to cast not only steel, but also aluminium and copper alloys. Since its widespread introduction for steel in the 1950s, it has evolved to achieve improved yield, quality, productivity and cost efficiency. It allows lower-cost production of metal sections with better quality, due to the inherently lower costs of continuous, standardized production of a product, as well as providing increased control over the process through automation. Nevertheless, challenges remain and new ones appear, as ways are sought to minimize casting defects and to cast alloys that could originally only be cast via other means. This Special Issue of the journal ""Metals"" consists of 14 research articles that cover many aspects of experimental work and theoretical modelling related to the ongoing development of continuous casting processes.

Keywords

slab continuous casting --- hybrid simulation model --- uneven secondary cooling --- numerical simulation --- molten steel flow --- solidification --- inclusion motion --- inclusion entrapment --- billet continuous casting --- swirling flow tundish --- multiphase flow --- heat transfer --- mold --- continuous casting --- numerical simulation --- round bloom --- continuous casting --- final electromagnetic stirring --- electromagnetic field --- polycrystalline model --- pores --- inclusions --- mechanism --- beam blank --- crystal --- propagation --- asymptotic analysis --- numerical simulation --- continuous casting --- air mist spray cooling --- continuous casting --- heat flux --- HTC --- secondary cooling --- thin-slab cast direct-rolling --- austenite grain coarsening --- grain growth control --- liquid core reduction --- secondary cooling --- two-phase pinning --- annular argon blowing --- upper nozzle --- flow behavior --- argon gas distribution --- tundish --- continuous casting --- bulge deformation --- thermomechanical coupling --- segmented roller --- finite element analysis --- steel tundish --- baffle --- flow field --- velocity --- PIV --- multi-source information fusion --- data stream --- continuous casting --- roll gap value --- prediction --- global optimization --- support vector regression --- variational mode decomposition --- empirical mode decomposition --- support vector regression --- mold level --- continuous casting --- magnetohydrodynamics --- fluid flow --- bubbles --- inclusions --- entrapment --- entrainment --- heat transfer --- solidification --- slab mold --- continuous casting --- n/a

Open-Source Electronics Platforms

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ISBN: 9783038979722 / 9783038979739 Year: Pages: 262 DOI: 10.3390/books978-3-03897-973-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Open-source electronics are becoming very popular, and are integrated with our daily educational and developmental activities. At present, the use open-source electronics for teaching science, technology, engineering, and mathematics (STEM) has become a global trend. Off-the-shelf embedded electronics such as Arduino- and Raspberry-compatible modules have been widely used for various applications, from do-it-yourself (DIY) to industrial projects. In addition to the growth of open-source software platforms, open-source electronics play an important role in narrowing the gap between prototyping and product development. Indeed, the technological and social impacts of open-source electronics in teaching, research, and innovation have been widely recognized.

Keywords

human-computer interface (HCI) --- electrooculogram (EOG) --- electromyogram (EMG) --- modified sliding window algorithm --- piecewise linear approximation (PLA) --- support vector regression --- eye tracking --- blockchain --- ontology --- context --- cyber-physical systems --- robotics --- interaction --- coalition --- individual management of livestock --- momentum data sensing --- remote sensing platform --- sensor networks --- technology convergence --- industry 4.0 --- distributed measurement systems --- automation networks --- node-RED --- cloud computing --- OPC UA --- hardware trojan taxonomy --- thermal imaging --- side channel analysis --- infrared --- FPGA --- Internet of Things --- wireless sensor networks --- Cloud of Things --- virtual sensor --- sensor detection --- smart cities --- Internet of Things --- Raspberry Pi --- BeagleBoard --- Arduino --- Internet of Things --- open hardware --- smart farming --- teaching robotics --- science teaching --- STEM --- robotic tool --- Python --- Raspberry Pi --- PiCamera --- vision system --- service learning --- robotics --- open platform --- automated vehicle --- EPICS --- open-source platform --- visual algorithms --- digital signal controllers --- embedded systems education --- dsPIC --- Java --- smart converter --- maximum power point tracking (MPPT) --- photovoltaic (PV) system --- Field Programmable Gate Array (FPGA) --- Digital Signal Processor (DSP) --- interleaved --- DC/DC converter --- distributed energy resource --- n/a

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

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ISBN: 9783039216642 / 9783039216659 Year: Pages: 294 DOI: 10.3390/books978-3-03921-665-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Meteorology and Climatology
Added to DOAB on : 2019-12-09 16:10:12
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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.

Keywords

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

Sediment Transport in Coastal Waters

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ISBN: 9783038978442 9783038978459 Year: Pages: 284 DOI: 10.3390/books978-3-03897-845-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Oceanography
Added to DOAB on : 2019-04-25 16:37:17
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The interface of 440,000 km long coastline in the world is subject to global change, with an increasing human pressure (land use, buildings, sand mining, dredging) and increasing population. Improving our knowledge on involved mechanisms and sediment transport processes, monitoring the evolution of sedimentary stocks and anticipating changes in littoral and coastal zones is essential for this purpose. The special issue of Water on “Sediment transport in coastal waters” gathers thirteen papers which introduce the current revolution in the scientific research related to coastal and littoral hydrosedimentary dynamics, and reflect the diversity of concerns on which research in coastal sediment transport is based, and current trends — topics and preferred methods — to address them.

Keywords

suspended sediment --- sediment transport --- coastal hydraulics --- Mekong --- river plume --- monsoon --- mathematical model --- geochemical map --- particle transfer process --- tidal current --- analysis of variance (ANOVA) --- Cluster analysis --- Mahalanobis’ generalized distances --- Seto Inland Sea --- East Coast Low --- nearshore processes --- coastal erosion --- coastal management --- climate change --- numerical modelling --- Southeast Australia --- soil erosion --- SWAT --- water scarcity --- sediment transport modelling --- Tafna catchment --- North Africa --- suspended sediment --- sediment transport --- lagoon --- geochemistry --- Ni mining --- sediment trap --- hydrodynamics --- New Caledonia --- dry season --- Senegal River delta --- Langue de Barbarie spit --- delta vulnerability --- river-mouth migration --- spit breaching --- ERA hindcast waves --- longshore sediment transport --- Vietnam --- South China Sea --- erosion --- recovery --- storminess --- winter monsoon --- typhoons --- shoreline --- waves forcing --- storms --- resilience --- post-storm recovery --- Bight of Benin --- seasonal cycle --- trend --- sand-mud mixture erosion --- numerical modelling --- non-cohesive to cohesive transition --- remote sensing reflectance --- turbidity --- seagrass beds --- bed shear stress --- fresh water runoff --- oceanic water intrusion --- suspended particulate matter --- aggregates --- flocculation --- biomass --- sediment --- turbidity --- remote-sensing --- MODerate Resolution Imaging Spectroradiometer (MODIS) --- Support Vector Regression (SVR) --- oligotrophic lagoon --- bathymetry --- reflectance --- seabed colour --- coral reef --- New Caledonia --- sediment transport --- cohesive sediments --- non cohesive sediments --- sand --- mud --- coastal erosion --- sedimentation --- morphodynamics --- suspended particulate matter --- bedload

Molecular Computing and Bioinformatics

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ISBN: 9783039211951 / 9783039211968 Year: Pages: 390 DOI: 10.3390/books978-3-03921-196-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-08-28 11:21:27
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This text will provide the most recent knowledge and advances in the area of molecular computing and bioinformatics. Molecular computing and bioinformatics have a close relationship, paying attention to the same object but working towards different orientations. The articles will range from topics such as DNA computing and membrane computing to specific biomedical applications, including drug R&D and disease analysis.

Keywords

prostate cancer --- Mycoplasma hominis --- endoplasmic reticulum --- systems biology --- protein targeting --- biomedical text mining --- big data --- Tianhe-2 --- parallel computing --- load balancing --- bacterial computing --- bacteria and plasmid system --- Turing universality --- recursively enumerable function --- miRNA biogenesis --- structural patterns --- DCL1 --- protein–protein interaction (PPI) --- clustering --- protein complex --- penalized matrix decomposition --- avian influenza virus --- interspecies transmission --- amino acid mutation --- machine learning --- Bayesian causal model --- causal direction learning --- K2 --- brain storm optimization --- line graph --- Cartesian product graph --- join graph --- atom-bond connectivity index --- geometric arithmetic index --- P-glycoprotein --- efflux ratio --- in silico --- machine learning --- hierarchical support vector regression --- absorption --- distribution --- metabolism --- excretion --- toxicity --- image encryption --- chaotic map --- DNA coding --- Hamming distance --- Stenotrophomonas maltophilia --- iron acquisition systems --- iron-depleted --- RAST server --- NanoString Technologies --- siderophores --- gene fusion data --- gene susceptibility prioritization --- evaluating driver partner --- gene networks --- drug-target interaction prediction --- machine learning --- drug discovery --- microRNA --- environmental factor --- structure information --- similarity network --- bioinformatics --- identification of Chinese herbal medicines --- biochip technology --- DNA barcoding technology --- DNA strand displacement --- cascade --- 8-bit adder/subtractor --- domain label --- Alzheimer’s disease --- gene coding protein --- sequence information --- support vector machine --- classification --- adverse drug reaction prediction --- heterogeneous information network embedding --- stacking denoising auto-encoder --- meta-path-based proximity --- Panax ginseng --- oligopeptide transporter --- flowering plant --- phylogeny --- transcription factor --- multiple interaction networks --- function prediction --- multinetwork integration --- low-dimensional representation --- dihydrouridine --- nucleotide physicochemical property --- pseudo dinucleotide composition --- RNA secondary structure --- ensemble classifier --- diabetes mellitus --- hypoxia-inducible factor-1? --- angiogenesis --- bone formation --- osteogenesis --- protein transduction domain --- membrane computing --- edge detection --- enzymatic numerical P system --- resolution free --- molecular computing --- molecular learning --- DNA computing --- self-organizing systems --- pattern classification --- machine learning --- laccase --- Brassica napus --- lignification --- stress --- molecular computing --- bioinformatics --- machine learning --- protein --- DNA --- RNA --- drug --- bio-inspired

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