Applied Mathematics for Restructured Electric Power Systems PDF Download
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Author: Joe H. Chow
Publisher: Springer
ISBN: 9781441936318
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence consists of chapters based on work presented at a National Science Foundation workshop organized in November 2003. The theme of the workshop was the use of applied mathematics to solve challenging power system problems. The areas included control, optimization, and computational intelligence. In addition to the introductory chapter, this book includes 12 chapters written by renowned experts in their respected fields. Each chapter follows a three-part format: (1) a description of an important power system problem or problems, (2) the current practice and/or particular research approaches, and (3) future research directions. Collectively, the technical areas discussed are voltage and oscillatory stability, power system security margins, hierarchical and decentralized control, stability monitoring, embedded optimization, neural network control with adaptive critic architecture, control tuning using genetic algorithms, and load forecasting and component prediction. This volume is intended for power systems researchers and professionals charged with solving electric and power system problems.
Author: Joe H. Chow
Publisher: Springer
ISBN: 9781441936318
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence consists of chapters based on work presented at a National Science Foundation workshop organized in November 2003. The theme of the workshop was the use of applied mathematics to solve challenging power system problems. The areas included control, optimization, and computational intelligence. In addition to the introductory chapter, this book includes 12 chapters written by renowned experts in their respected fields. Each chapter follows a three-part format: (1) a description of an important power system problem or problems, (2) the current practice and/or particular research approaches, and (3) future research directions. Collectively, the technical areas discussed are voltage and oscillatory stability, power system security margins, hierarchical and decentralized control, stability monitoring, embedded optimization, neural network control with adaptive critic architecture, control tuning using genetic algorithms, and load forecasting and component prediction. This volume is intended for power systems researchers and professionals charged with solving electric and power system problems.
Author: S. Smys
Publisher: Springer Nature
ISBN: 9813343052
Category : Technology & Engineering
Languages : en
Pages : 983
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Book Description
This book is a collection of best selected papers presented at the International Conference on Inventive Computation and Information Technologies (ICICIT 2020), organized during 24–25 September 2020. The book includes papers in the research area of information sciences and communication engineering. The book presents novel and innovative research results in theory, methodology and applications of communication engineering and information technologies.
Author: S.A. Soliman
Publisher: Elsevier
ISBN: 0123815444
Category : Business & Economics
Languages : en
Pages : 441
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Book Description
Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world’s foremost experts on the subject, Electrical Load Forecasting provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. The book begins with a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. The book is also supported by an online computer program that allows readers to construct, validate, and run short and long term models. Step-by-step guide to model construction Construct, verify, and run short and long term models Accurately evaluate load shape and pricing Creat regional specific electrical load models
Author: Kedar Nath Das
Publisher: Springer Nature
ISBN: 9811500355
Category : Technology & Engineering
Languages : en
Pages : 994
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Book Description
This two-volume book presents the outcomes of the 8th International Conference on Soft Computing for Problem Solving, SocProS 2018. This conference was a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), and Vellore Institute of Technology (India), and brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions. The book highlights the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers on algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It offers a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that are difficult to solve using traditional methods.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668424096
Category : Computers
Languages : en
Pages : 1575
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Book Description
Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.
Author: Jose Mira
Publisher: Springer Science & Business Media
ISBN: 9783540594970
Category : Computers
Languages : en
Pages : 1182
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Book Description
This volume presents the proceedings of the International Workshop on Artificial Neural Networks, IWANN '95, held in Torremolinos near Malaga, Spain in June 1995. The book contains 143 revised papers selected from a wealth of submissions and five invited contributions; it covers all current aspects of neural computation and presents the state of the art of ANN research and applications. The papers are organized in sections on neuroscience, computational models of neurons and neural nets, organization principles, learning, cognitive science and AI, neurosimulators, implementation, neural networks for perception, and neural networks for communication and control.
Author: Filippo Maria Bianchi
Publisher: Springer
ISBN: 3319703382
Category : Computers
Languages : en
Pages : 74
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Book Description
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Author: Maria Jacob
Publisher: Springer Nature
ISBN: 303028669X
Category : Mathematics
Languages : en
Pages : 108
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Book Description
The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general.
Author: Dejan J. Sobajic
Publisher: Psychology Press
ISBN: 1134781970
Category : Psychology
Languages : en
Pages : 246
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Book Description
Power system computing with neural networks is one of the fastest growing fields in the history of power system engineering. Since 1988, a considerable amount of work has been done in investigating computing capabilities of neural networks and understanding their relevance to providing efficient solutions for outstanding complex problems of the electric power industry. A principal objective of a power utility is to provide electric energy to its customers in a secure, reliable and economic manner. Toward this aim, utility personnel are engaged in a variety of activities in areas of supervisory control and monitoring, evaluation of operating conditions, operation planning and scheduling, system development, equipment testing, etc. Over the past decades significant advances have been made in the development of new concepts, design of hardware and software systems, and implementation of solid-state devices which all contributed to the steadily improving power system performance that we are experiencing today. Advanced information processing technologies played an important role in these development efforts. Members of the Special Interest Group for Power Engineering of the INNS recognized the need for bringing together leading researchers in the field of neurocomputing with experts from power utilities and manufacturing companies to assess the current state of affairs and to explore the directions of further research and practice. This book is based on The Summer Workshop on Neural Network Computing for the Electric Power Industry which brought together approximately forty specialists with backgrounds in power engineering, system operation and planning, neural network theory and AI systems design. An informal and highly inspiring atmosphere of the workshop facilitated open discussion and exchange of expertise between the participants.
Author: Yi Wang
Publisher: Springer Nature
ISBN: 9811526249
Category : Business & Economics
Languages : en
Pages : 306
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Book Description
This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.
