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Author: Derek W. Bunn
Publisher:
Published: 1985
Total Pages: 256
ISBN-13:
DOWNLOAD EBOOK →Takes a practical look at how short-term forecasting has actually been undertaken and is being developed in public utility organizations.
Author: S.A. Soliman
Publisher: Elsevier
Published: 2010-05-26
Total Pages: 441
ISBN-13: 0123815444
DOWNLOAD EBOOK →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: Filippo Maria Bianchi
Publisher: Springer
Published: 2017-11-09
Total Pages: 72
ISBN-13: 3319703382
DOWNLOAD EBOOK →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:
Publisher:
Published: 1904
Total Pages:
ISBN-13:
DOWNLOAD EBOOK →Aplicação de duas metodologias baseadas em estatísticas adaptativas, com a finalidade de modelar e prever o comportamento de uma série temporal (série histórica de carga elétrica horária) gerada pela concessionária de energia elétrica Light. Foi aplicada à série de carga elétrica horária a metodologia de amortecimento direto, utilizada para a previsão horária e diária de carga e o modelo de previsão adaptativa de carga elétrica horária de curto prazo (GUPTA, P.C.), utilizado para a previsão diária de carga. É demonstrado o bom desempenho do método de amortecimento direto na previsão horária de carga elétrica. Na previsão diária, o modelo de previsão adaptativa de curto prazo de cargas elétricas horárias (GUPTA, P.C) apresenta resultados superiores aos do método de amortecimento direto.
Author: Derek W. Bunn
Publisher:
Published: 1985
Total Pages: 256
ISBN-13:
DOWNLOAD EBOOK →Takes a practical look at how short-term forecasting has actually been undertaken and is being developed in public utility organizations.
Author: Hla-U-May Marma
Publisher:
Published: 2020
Total Pages:
ISBN-13:
DOWNLOAD EBOOK →In this thesis, a transfer function-based load model is determined for commercial and industrial load. This model is derived from the composite load model which consist of an induction motor and static load. This developed model is compared to composite load model by considering two cases: 1) a small motor composition load or commercial load and 2) higher motor composition load or industrial load. The research is conducted through MATLAB/Simulink simulation. In order to compare the dynamic response of developed model, a comparative study has been done between the two models. In addition, the influence of voltage and frequency dependency terms on the overall model accuracy for developed model has been evaluated through several case studies considering both voltage and frequency dependency disturbances. A short-term load forecast model is developed for an electrically heated house. This research work is based on experimental data collected by installing current sensors in a house in St. Johns, Newfoundland, Canada. The data was collected for three years and only one-year data is used for this model. The model is based on Recurrent Neural Network (RNN) with wavelet transform. The proposed model is verified by comparing other developed models in the literature through MATLAB deep learning toolbox and wavelet toolbox. The proposed model can more accurately forecast the load.
Author: H. Lee Willis
Publisher: CRC Press
Published: 2002-08-09
Total Pages: 770
ISBN-13: 9780203910764
DOWNLOAD EBOOK →Containing 12 new chapters, this second edition offers increased coverage of weather correction and normalization of forecasts, anticipation of redevelopment, determining the validity of announced developments, and minimizing risk from over- or under-planning. It provides specific examples and detailed explanations of key points to consider for both standard and unusual utility forecasting situations, information on new algorithms and concepts in forecasting, a review of forecasting pitfalls and mistakes, case studies depicting challenging forecast environments, and load models illustrating various types of demand.
Author: Paul S.P. Cowpertwait
Publisher: Springer Science & Business Media
Published: 2009-05-28
Total Pages: 262
ISBN-13: 0387886982
DOWNLOAD EBOOK →This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.
Author: Antonio Gabaldón
Publisher: MDPI
Published: 2021-02-26
Total Pages: 324
ISBN-13: 303943442X
DOWNLOAD EBOOK →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.
