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Short Term Load Forecasting An Attempt To Use Artificial Neural Networks
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Book Synopsis Short term load forecasting - an attempt to use artificial neural networks by :
Download or read book Short term load forecasting - an attempt to use artificial neural networks written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A previsão de perfis de carga elétrica (i.e., das séries de cargas a cada hora de um dia) tem sido freqüentemente tentada por meio de modelos baseados em redes neurais. Os resultados conseguidos por estes modelos, contudo, ainda não são considerados inteiramente convincentes. Há duas razões para ceticismo: em primeiro lugar, os modelos sugeridos geralmente se baseiam em redes que parecem ser complexas demais em relação aos dados que pretendem modelar (isto é, estes modelos parecem estar superparametrizados); em segundo lugar, estes modelos geralmente não são bem validados, pois os artigos que os propõem não comparam o desempenho das redes ao de modelos de referência. Nesta tese, examinamos estes dois pontos por meio de revisões críticas da literatura e de simulações, a fim de verificar se é realmente viável a aplicação de redes neurais à previsão de perfis de carga. Nas simulações, construímos modelos bastante complexos de redes e verificamos empiricamente sua validade, pela comparação de seu desempenho preditivo fora da amostra de treino ao desempenho de vários outros modelos de previsão. Os resultados mostram que as redes, mesmo quando muito complexas, conseguem previsões de perfis mais acuradas do que os modelos tradicionais, o que sugere que elas poderão trazer uma grande contribuição para a solução do problema de previsão de cargas.
Book Synopsis Recurrent Neural Networks for Short-Term Load Forecasting by : Filippo Maria Bianchi
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.
Book Synopsis Short Term Load Forecasting Using Artificial Neural Networks by : Syed Mahmood Ahmed
Download or read book Short Term Load Forecasting Using Artificial Neural Networks written by Syed Mahmood Ahmed and published by . This book was released on 1992 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Short-Term Load Forecasting by Artificial Intelligent Technologies by : Wei-Chiang Hong
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
Book Synopsis Short Term Load Forecasting Using Artificial Neural Networks by : Andrew Rae
Download or read book Short Term Load Forecasting Using Artificial Neural Networks written by Andrew Rae and published by . This book was released on 1996 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Short-term Electric Load Forecasting Using Artificial Neural Networks by : Eric Lee Daugherty
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:
Book Synopsis Short Term Load Forecasting Using Artificial Neural Network by : Lawerence Chong
Download or read book Short Term Load Forecasting Using Artificial Neural Network written by Lawerence Chong and published by . This book was released on 1992 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Application of Artificial Neural Networks to Short Term Load Forecasting by : C. Hart Poskar
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:
Book Synopsis Power System Peak Load Forecasting Using an Artifical Neural Network by : John Cass
Download or read book Power System Peak Load Forecasting Using an Artifical Neural Network written by John Cass and published by . This book was released on 1996 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examines several models for short term load forecasting for the Rockhampton Area during the first six months of 1995. Artificial Neural Networks (ANN) were used.
Book Synopsis Development of Models for Short-term Load Forecasting Using Artificial Neural Networks by : Simakeka Amakali
Download or read book Development of Models for Short-term Load Forecasting Using Artificial Neural Networks written by Simakeka Amakali and published by . This book was released on 2008 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Neural Network for Short Term Load Forecasting Using Seasonal Partitioning Method by : Didin Mardijono
Download or read book Artificial Neural Network for Short Term Load Forecasting Using Seasonal Partitioning Method written by Didin Mardijono and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Short Term Load Forecasting Using Artificial Neural Networks by : Iskandar Kfoury
Download or read book Short Term Load Forecasting Using Artificial Neural Networks written by Iskandar Kfoury and published by . This book was released on 2015 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Short-term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs by :
Download or read book Short-term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.
Book Synopsis Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Multivariable Inputs by : Jaime H. Buitrago
Download or read book Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Multivariable Inputs written by Jaime H. Buitrago and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1\% have been achieved, which is a 30\% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. In addition, in order to improve the robustness of the forecast to variations in the number of neurons and other network parameters, the author proposes a method using an exponential decay of the error weights for training the neural network. The modification consists in giving higher error weight to more recent values and lower weight to older values of the training set. By doing this, mover recent values have a higher influence on the calculation of the synaptic weights and therefore the forecast produced by the NARX network is more accurate. This method, combined with the use of Bayesian regularization for training, results in improved forecast accuracy of up to 25\% and robustness to variation in parameter selection. The New England electrical load data are used to train and validate the forecast prediction.
Book Synopsis Short-term Electric Load Forecasting by Using Multi-layer Feed-forward Neural Network by : Marvin Herbert Wibisono
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:
Book Synopsis Short Term Load Forecasting Using a Neural Network Based Time Series Approach by : Suci Dwijayanti
Download or read book Short Term Load Forecasting Using a Neural Network Based Time Series Approach written by Suci Dwijayanti and published by . This book was released on 2013 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A short-term load forecasting model using neural network and fuzzy logic by :
Download or read book A short-term load forecasting model using neural network and fuzzy logic written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
