Download or read book Applications of Neural Networks to Electric Short Term Load Forecasting written by Maryam Poursardar and published by . This book was released on 1995 with total page 140 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 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 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 Smart Meter Data Analytics written by Yi Wang and published by Springer Nature. This book was released on 2020-02-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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 The Application of Artificial Neural Networks to Short Term Electrical Load Forecasting and Other Engineering Problems written by Azzam-ul-Asar and published by . This book was released on 1994 with total page 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 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 Short term Electric Load Forecasting by Using Multi layer Feed forward Neural Network written by Marvin Herbert Wibisono and published by . This book was released on 2004 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural networks in load forecasting in electric energy systems written by and published by . This book was released on 1908 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Esta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na área de previsão de carga elétrica. Nesta investigação foram utilizados dados reais de energia relativos ao sistema elétrico brasileiro. O trabalho consiste de quatro partes principais: um estudo sobre o problema de previsão de carga no contexto de sistemas elétricos de potência; o estudo e a modelagem das RNAs para previsão de carga; o desenvolvimento do ambiente de simulação; e o estudo de casos. O estudo sobre o problema de previsão de carga envolveu uma investigação sobre a importância da previsão de demanda de energia na área de sistemas elétricos de potência. Enfatizou-se a classificação dos diversos tipos de previsão de acordo com o seu horizonte, curto e longo prazo, bem como a análise das variáveis mais relevantes para a modelagem da carga elétrica. O estudo também consistiu da análise de vários projetos na área de previsão de carga, apresentando as metodologias mais utilizadas. O estudo e a modelagem de RNAs na previsão de carga envolveu um extenso estudo bibliográfico de diversas metodologias. Foram estudadas as arquiteturas e os algoritmos de aprendizado mais empregados. Constatou-se uma predominância da utilização do algoritmo de retropropagação (Backpropagation) nas aplicações de previsão de carga elétrica horária para curto prazo. A partir desse estudo, e utilizando o algoritmo de retropropagação, foram propostas diversas arquiteturas de RNAs de acordo com o tipo de previsão desejada. O desenvolvimento do ambiente de simulação foi implementado em linguagem C em estações de trabalho SUN. O pacote computacional engloba basicamente 3 módulos: um módulo de pré-processamento da série de carga para preparar os dados de entrada; um módulo de treinamento da Rede Neural para o aprendizado do comportamento da série; e um módulo de execução da Rede Neural para a previsão dos valores futuros da série. A construção de uma interface amigável para a execução do sistema de previsão, bem como a obtenção de um sistema portátil foram as metas principais para o desenvolvimento do simulador. O estudo de casos consistiu de um conjunto de implementações com o objetivo de testar o desempenho de um sistema de previsão baseado em Redes Neurais para dois horizontes distintos: previsão horária e previsão mensal. No primeiro caso, foram utilizados dados de energia da CEMIG (Estado de Minas Gerais) e LIGHT (Estado do Rio de Janeiro). No segundo caso, foram utilizados dados de energia de 32 companhias do setor elétrico brasileiro. Destaca-se que a previsão mensal faz parte de um projeto de interesse da ELETROBRÁS, contratado pelo CEPEL. Para ambos os casos, investigou-se a influência do horizonte de previsão e da época do ano no desempenho do sistema de previsão. Além disso, foram estudadas as variações do desempenho das Redes Neurais de acordo com a empresa de energia elétrica utilizada. A avaliação do desempenho foi feita através da análise das seguintes estatísticas de erro: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Error) e U de Theil. O desempenho das RNAs foi comparado com o de outras técnicas de previsão, como os métodos de Holt-Winters e Box & Jenkins, obtendo-se resultados, em muitos casos, superiores.

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 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 The Application of Artificial Neural Networks to Short Term Load Forecasting written by C. Hart Poskar and published by . This book was released on 1993 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Core Concepts and Methods in Load Forecasting written by Stephen Haben and published by Springer Nature. This book was released on 2023-06-01 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code). Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital. This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.

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 Short term Electric Load Forecasting Using Neural Network Models written by Yasser Al-Rashid and published by . This book was released on 1995 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: