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Effect of Load Contribution Factor on Multinodal Load Forecasting
IEEE EUROCON 2021 - 19th International Conference on Smart Technologies, 2021This paper discusses a load contribution factor (LCF) based multinodal load forecasting technique. The dynamic nature of the electrical load in any distribution network is the reason behind the need for simultaneous forecasting of load at all the nodes.
Sneha Rai, Mala De
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Probabilistic Load Forecasting
2021Probabilistic load forecasting (PLF) is able to present the uncertainty information of the future loads. It is the basis of stochastic power system planning and operation. Recent works on PLF mainly focus on how to develop and combine forecasting models; while the feature selection issue has not been thoroughly investigated for PLF.
Qixin Chen +3 more
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Methodologies for Load Forecasting
2006 3rd International IEEE Conference Intelligent Systems, 2006The ability to accurately forecast Load is vitally important for the electric industry in a deregulated economy. Load forecasting has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. A large variety of methods have been developed for and applied to load forecasting.
Piers R. J. Campbell, Ken Adamson
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Load Forecasting Accuracy through Combination of Trimmed Forecasts
2012Neural network (NN) models have been receiving considerable attention and a wide range of publications regarding short-term load forecasting have been reported in the literature. Their popularity is mainly due to their excellent learning and approximation capabilities.
Saima Hassan +3 more
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IEEE Computer Applications in Power, 1995
The reliability, efficiency, and economy of a power delivery system depend mainly on how well its substations, transmission lines, and distribution feeders are located within the utility service area, and how well their capacities match power needs in their respective localities. Often, utility planners are forced to commit to sites, rights of way, and
H.L. Willis, M.V. Engel, M.J. Buri
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The reliability, efficiency, and economy of a power delivery system depend mainly on how well its substations, transmission lines, and distribution feeders are located within the utility service area, and how well their capacities match power needs in their respective localities. Often, utility planners are forced to commit to sites, rights of way, and
H.L. Willis, M.V. Engel, M.J. Buri
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IEEE Computer Applications in Power, 1993
A description of artificial neural networks (ANNs) is given. Reasons why interest in ANNs has increased are discussed. Steps used to train neural networks (NNs) are described, including gathering and normalizing data, selecting NN architecture, training and testing networks, selecting alternative network architectures, and performing additional ...
D.D. Highley, T.J. Hilmes
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A description of artificial neural networks (ANNs) is given. Reasons why interest in ANNs has increased are discussed. Steps used to train neural networks (NNs) are described, including gathering and normalizing data, selecting NN architecture, training and testing networks, selecting alternative network architectures, and performing additional ...
D.D. Highley, T.J. Hilmes
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Advanced Load Forecast with hierarchical forecasting capability
2013 IEEE Power & Energy Society General Meeting, 2013Advanced Load Forecast (ALF) is a novel load forecast application that integrates different instances of Artificial Neural Network (ANN) forecast engines and generates load forecasts for multiple load locations at various hierarchical levels. The forecast results can be aggregated up and distributed down the hierarchical structure.
null Wei Guan +6 more
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IEEE Control Systems, 2007
This article illustrates the application of a nonlinear system identification technique to the problem of STLF. Five NARX models are estimated using fixed-size LS-SVM, and two of the models are later modified into AR-NARX structures following the exploration of the residuals.
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This article illustrates the application of a nonlinear system identification technique to the problem of STLF. Five NARX models are estimated using fixed-size LS-SVM, and two of the models are later modified into AR-NARX structures following the exploration of the residuals.
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1995
Application of artificial neural networks (ANNs) to forecast the hourly loads of an electrical power system is examined in this chapter. Two types of ANN’s, i.e., the Kohonen’s self-organising feature maps and the feedforward multilayer neural networks, are employed for load forecasting.
Yuan-Yih Hsu, Chien-Chun Yang
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Application of artificial neural networks (ANNs) to forecast the hourly loads of an electrical power system is examined in this chapter. Two types of ANN’s, i.e., the Kohonen’s self-organising feature maps and the feedforward multilayer neural networks, are employed for load forecasting.
Yuan-Yih Hsu, Chien-Chun Yang
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2012
A focus on a practical implemented case study presents an added value for the better appreciation of this topic.
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A focus on a practical implemented case study presents an added value for the better appreciation of this topic.
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