Short Term Electric Load Forecasting Using Neural Network With Fuzzy Set Based Classification
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Short Term Electric Load Forecasting Using Neural Network With Fuzzy Set Based Classification
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Author : Gumpanart Bumroonggit
language : en
Publisher:
Release Date : 1995
Short Term Electric Load Forecasting Using Neural Network With Fuzzy Set Based Classification written by Gumpanart Bumroonggit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Electrical Load Forecasting
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Author : S.A. Soliman
language : en
Publisher: Elsevier
Release Date : 2010-05-26
Electrical Load Forecasting written by S.A. Soliman and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-26 with Business & Economics categories.
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
Short Term Load Forecasting By Artificial Intelligent Technologies
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Author : Wei-Chiang Hong
language : en
Publisher: MDPI
Release Date : 2019-01-29
Short Term Load Forecasting By Artificial Intelligent Technologies written by Wei-Chiang Hong and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with categories.
This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Hybrid Advanced Techniques For Forecasting In Energy Sector
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Author : Wei-Chiang Hong
language : en
Publisher: MDPI
Release Date : 2018-10-19
Hybrid Advanced Techniques For Forecasting In Energy Sector written by Wei-Chiang Hong and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-19 with Electronic books categories.
This book is a printed edition of the Special Issue "Hybrid Advanced Techniques for Forecasting in Energy Sector" that was published in Energies
A Short Term Load Forecasting Model Using Neural Network And Fuzzy Logic
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Author :
language : pt-BR
Publisher:
Release Date : 2003
A Short Term Load Forecasting Model Using Neural Network And Fuzzy Logic written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
O objetivo principal desta dissertação é desenvolver um método de previsão de carga elétrica de curto prazo (previsão horária), através de um sistema híbrido(Redes Neurais e Lógica Fuzzy) utilizando temperaturas máximas e mínimas como variáveis explicativas. Como primeiro passo, foram definidos os perfis homogêneos das curvas de carga diárias através de um classificador utilizando os Mapas Auto Organizáveis (Self-Organizing Maps-SOM). Um previsor será adicionado ao esquema de previsão através da Lógica Fuzzy que associará as variáveis climáticas aos perfis criados pela SOM produzindo as previsões. O modelo foi aplicado em dados de duas concessionárias de energia elétrica do Brasil usando dados horários coletados durante dois anos.
Proceedings Of The National Seminar On Applied Systems Engineering And Soft Computing
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Author :
language : en
Publisher: Allied Publishers
Release Date : 2000
Proceedings Of The National Seminar On Applied Systems Engineering And Soft Computing written by and has been published by Allied Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Soft computing categories.
Recurrent Neural Networks For Short Term Load Forecasting
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Author : Filippo Maria Bianchi
language : en
Publisher: Springer
Release Date : 2017-11-09
Recurrent Neural Networks For Short Term Load Forecasting written by Filippo Maria Bianchi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-09 with Computers categories.
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.
Short Term Load Forecasting Using Fuzzy Neural Networks
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Author : Haiwu Ma
language : en
Publisher:
Release Date : 1994
Short Term Load Forecasting Using Fuzzy Neural Networks written by Haiwu Ma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Electric power-plants categories.
Short Term Load Forecasting 2019
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Author : Antonio Gabaldón
language : en
Publisher: MDPI
Release Date : 2021-02-26
Short Term Load Forecasting 2019 written by Antonio Gabaldón and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.
Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
Electric Load Forecasting Using An Artificial Neural Networks
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Author : Natalia Gotman
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2014-03
Electric Load Forecasting Using An Artificial Neural Networks written by Natalia Gotman and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03 with categories.
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.
