Download or read book Short term Electric Load Forecasting Using Artificial Neural Networks written by Eric Lee Daugherty and published by . This book was released on 1994 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Electric Load Forecasting Using an Artificial Neural Networks written by Natalia Gotman and published by LAP Lambert Academic Publishing. This book was released on 2014-03 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day - operating forecasting; one-day-week-month-ahead load forecasting - short-term load forecasting; one-month-quarter-year-ahead load forecasting - long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.
Download or read book Inventive Computation and Information Technologies written by S. Smys and published by Springer Nature. This book was released on 2021-03-27 with total page 983 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Nebraska Electric Load Forecasting Using Artificial Neural Networks with C written by Shujun Hou and published by . This book was released on 1994 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Recurrent Neural Networks for Short Term Load Forecasting written by Filippo Maria Bianchi and published by Springer. This book was released on 2017-11-09 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Electrical Load Forecasting written by S.A. Soliman and published by Elsevier. This book was released on 2010-05-26 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Applied Mathematics for Restructured Electric Power Systems written by Joe H. Chow and published by Springer Science & Business Media. This book was released on 2006-06-03 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Computational Intelligence Applications in Modeling and Control written by Ahmad Taher Azar and published by Springer. This book was released on 2014-12-26 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 16 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.
Download or read book Forecasting Electricity Load in New Jersey with Artificial Neural Networks written by Erik W. Raab and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Load forecasting is an important tool for both the energy and environmental sectors. It has progressed hand-in-hand with machine learning innovation, where recurrent neural networks, a type of artificial neural network, is primarily used. This thesis compares progressively complex, feed-forward artificial neural networks using a mix of weather and temporal data. We demonstrate that electrical load in New Jersey can be reliably predicted using memory-less algorithms with minimal predictors drawn from preexisting public data sources. The methods used in this thesis could be used to build competitive load forecasting models in other states, and if included in diverse model ensembles, may generate significant improvements.
Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Download or read book Hourly Load Forecasting Using Artificial Neural Networks written by Alireza Khotanzad and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Short Term Load Forecasting by Artificial Intelligent Technologies written by Wei-Chiang Hong and published by MDPI. This book was released on 2019-01-29 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Download or read book Intelligent Renewable Energy Systems written by Neeraj Priyadarshi and published by John Wiley & Sons. This book was released on 2022-01-19 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTELLIGENT RENEWABLE ENERGY SYSTEMS This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology. Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. Audience Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.
Download or read book Research Anthology on Artificial Neural Network Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2021-07-16 with total page 1575 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Short term Electric Load Forecasting Using Neural Networks written by and published by . This book was released on 1993 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term electric load forecasting (STELF) plays an important role in electric utilities, and several techniques are used to perform these predictions and system modelings. Recently, artificial neural networks (ANN's) have been implemented for STELF with some success. This paper will examine improved STELF by optimization of ANN techniques. The strategy for the research involves careful selection of input variables and utilization of effective generalization. Some results have been obtained which show that, with the selection of another input variable, the ANN's use for STELF can be improved.
Download or read book Short term Electrical Load Forecasting for an Institutional industrial Power System Using an Artificial Neural Network written by Eric Lynn Taylor and published by . This book was released on 2013 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: For optimal power system operation, electrical generation must follow electrical load demand. The generation, transmission, and distribution utilities require some means to forecast the electrical load so they can utilize their electrical infrastructure efficiently, securely, and economically. The short-term load forecast (STLF) represents the electric load forecast for a time interval of a few hours to a few days. This thesis will define STLF as a 24-hour-ahead load forecast whose results will provide an hourly electric load forecast in kilowatts (kW) for the future 24 hours (a 24-hour load profile). This thesis will use the method of Artificial Neural Networks (ANN) to create a STLF algorithm for the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL). ORNL’s power system can be described as an institutional/industrial-type electrical load. The ANN is a mathematical tool that mimics the thought processes of the human brain. The ANN can be created and trained to receive historical load and future weather forecasts as input and produce a load forecast as its output. Most ANNs in the literature are used to forecast the next day 24-hour load profile for a transmission-level system with resulting load forecast errors ranging from approximately 1 % to 3 %. This research will show that an ANN can be used to forecast the smaller, more chaotic load profile of an institutional/industrial-type power system and results in a similar forecast error range. In addition, the operating bounds of the ORNL electric load will be analyzed along with the weather profiles for the site. Correlations between load and weather and load and calendar descriptors, such as day of week and month, will be used as predictor inputs to the ANN to optimize is size and accuracy.
Download or read book From Natural to Artificial Neural Computation written by Jose Mira and published by Springer Science & Business Media. This book was released on 1995-05-24 with total page 1182 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
