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Forecasting data for load flow
Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521), 2004In order to achieve a more secure and economic operation of a power system its state should be known not only in the present but some time ahead. Transmission system operators do not have the same security margins like they had in the past. Most of the tools of security assessment operate on-line and provide valuable input to the system operators ...
Delimar, Marko +2 more
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Short-term load forecasting based on load profiling
2013 IEEE Power & Energy Society General Meeting, 2013Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility
Ramos, Sérgio +3 more
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Detection, mining and forecasting of impact load in power load forecasting
Applied Mathematics and Computation, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jianzhou, Ma, Zhixin, Li, Lian
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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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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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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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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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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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Forecasting load‐duration curves
Journal of Forecasting, 1994AbstractA new method is proposed for forecasting electricity load‐duration curves. The approach first forecasts the load curve and then uses the resulting predictive densities to forecast the load‐duration curve. A virtue of this procedure is that both load curves and load‐duration curves can be predicted using the same model, and confidence intervals ...
Andrew Bruce, Simon Jurke, Peter Thomson
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Probabilistic Residential Load Forecasting
2020The installation of smart meters enables the collection of massive fine-grained electricity consumption data and makes individual consumer level load forecasting possible. Compared to aggregated loads, load forecasting for individual consumers is prone to non-stationary and stochastic features.
Yi Wang, Qixin Chen, Chongqing Kang
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