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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.

Structural Health Monitoring (SHM) of Civil Structures

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ISBN: 9783038427834 9783038427841 Year: Pages: X, 490 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering
Added to DOAB on : 2018-04-20 14:47:20
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At the current time of writing, the American Society of Civil Engineers (ASCE) has awarded American infrastructure a grade of D+, meaning poor and at risk. Part of the reason for the low grade is due to the rapid deterioration of structural integrity and the inability of most places to safely meet future demands. Deficiencies in these areas may be remediated by advancements in structural health monitoring (SHM) technologies that provide sensing systems to automatically and economically diagnose structural integrity. In a sense, SHM technologies will help pave the way to intelligent structures that are able to detect damage by themselves and even warn occupants of any danger due to impending structural failure. Engineering sensors and developing smart algorithms for SHM often involves the close collaboration of a surprisingly large breadth of specialties. In this book, we have collected a thin but representative slice of the most recent research in SHM, and hope that the reader will gain an inspiring view of today’s research landscape and a notion of what is to come.

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.

Emerging Technologies for Electric and Hybrid Vehicles

ISBN: 9783038971900 9783038971917 Pages: 372 DOI: 10.3390/books978-3-03897-191-7
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Transportation --- Electrical and Nuclear Engineering
Added to DOAB on : 2018-10-17 09:07:11
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In a world where energy conservation, environmental protection and sustainable development are growing concerns, the development of electric vehicle (EV) and hybrid EV (HEV) technologies has taken on an accelerated pace. This collection entitled “Electric and Hybrid Vehicles” invites articles that address the state-of-the-art technologies and new developments for EVs and HEVs, including but not limited to energy sources, electric powertrains, hybrid powertrains, energy management systems, energy refueling systems, regenerative braking systems, system integration, system optimization and infrastructure. Articles which deal with the latest hot topics for EVs and HEVs are particularly encouraged such as advanced lithium-ion batteries, ultracapacitors, energy-efficient motor drives, bidirectional power converters, integrated-starter-generator systems, electric variable transmission systems, on-board renewable energy, inductive or wireless charging technology, and vehicle-to-grid technology. As the impact of the use of EVs and HEVs on our daily lives is utmost important, articles which deal with the relationships between the use of EVs or HEVs and the energy, environment and economy would be of particular interest.

Recent Developments in Cointegration

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ISBN: 9783038429555 9783038429562 Year: Pages: VIII, 210 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Mathematics
Added to DOAB on : 2018-07-05 13:20:18
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The Cointegrated VAR model allows the user to study both long-run and short-run effects in the same model. It describes an economic system where variables have been pushed away from long-run equilibria by exogenous shocks (the pushing forces) and where short-run adjustments forces pull them back toward long-run equilibria (the pulling forces). In this model framework, basic assumptions underlying an economic theory model can be translated into testable hypotheses of the order of integration and cointegration of key variables and their relationships. While the latter used to be I(1), macroeconomic and financial data have recently shown a tendency for puzzling long and persistent swings around long-run equilibrium values typical of self-reinforcing feed-back mechanisms. Such persistent fluctuations are frequently indistinguishable from I(2) data, pointing to the need for new econometric solutions. In this book, many of our most distinguished scholars in the field of cointegration offer a variety of solutions to these problems by formulating new models, tests, and asymptotics more suitable for an I(2) world. Several of the papers apply these cointegration techniques to a variety of empirical problems, thereby showing how to obtain valuable information about some of the mechanisms that have generated the recent crises.

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.

Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery

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ISBN: 9783038420521 9783038420538 Year: Pages: XIV, 228 DOI: 10.3390/books978-3-03842-053-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2015-10-22 07:34:37
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Detection and tracking of targets in forward looking infrared (FLIR) imagery are challenging tasks. IR sensors often provide low signal-to-noise ratio and heavy background cluttering images. Non-stationary cameras can introduce further challenges, because detection and tracking might make it necessary to properly deal with sensor ego-motion through suitable estimation and compensation techniques. Moreover, further issues are posed by imagery with multiple and possibly moving target and non-target objects, which can blend into the background, change their signature, size, shape, and even overlap during their motion. Finally, specific applications could introduce cumbersome real-time constraints, thus requiring tracking techniques with a reduced computational footprint.The objective of this Special Issue is to invite high state-of-the-art research contributions, tutorials, and position papers that address the broad challenges faced in analysis and processing of FLIR imagery. Original papers describing completed and unpublished work that are not currently under review by any other journal/magazine/conference/special issue are solicited.

First-Principles Prediction of Structures and Properties in Crystals

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ISBN: 9783039216703 / 9783039216710 Year: Pages: 128 DOI: 10.3390/books978-3-03921-671-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Chemistry (General)
Added to DOAB on : 2019-12-09 16:10:12
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The term “first-principles calculations” is a synonym for the numerical determination of the electronic structure of atoms, molecules, clusters, or materials from ‘first principles’, i.e., without any approximations to the underlying quantum-mechanical equations. Although numerous approximate approaches have been developed for small molecular systems since the late 1920s, it was not until the advent of the density functional theory (DFT) in the 1960s that accurate “first-principles” calculations could be conducted for crystalline materials. The rapid development of this method over the past two decades allowed it to evolve from an explanatory to a truly predictive tool. Yet, challenges remain: complex chemical compositions, variable external conditions (such as pressure), defects, or properties that rely on collective excitations—all represent computational and/or methodological bottlenecks. This Special Issue comprises a collection of papers that use DFT to tackle some of these challenges and thus highlight what can (and cannot yet) be achieved using first-principles calculations of crystals.

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