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Combining Probability Density Forecasts for Power Electrical Loads
IEEE Transactions on Smart Grid, 2020Researchers have proposed various probabilistic load forecasting models in the form of quantiles, densities, or intervals to describe the uncertainties of future energy demand. Density forecasts can provide more uncertainty information than can be expressed by just the quantile and interval. However, the combining method for density forecasts is seldom
Tianyi Li 0009 +2 more
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Application of Grey Forecasting Model to Load Forecasting of Power System
2011The grey GM (1, 1) model is a kind of more effective load forecasting model, however, because power load has diversity, causing some variation is larger, the load forecasting error cannot match the requirements. Precision in practical application has certain limitations.
Yan Yan +4 more
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Power management by load forecasting in web server clusters
Cluster Computing, 2011The complexity and requirements of web applications are increasing in order to meet more sophisticated business models (web services and cloud computing, for instance). For this reason, characteristics such as performance, scalability and security are addressed in web server cluster design.
Carlos Santana +2 more
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Forecasting loads and prices in competitive power markets
Proceedings of the IEEE, 2000This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations ...
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Power Load Forecasting Based on VMD and Attention-LSTM
Proceedings of the 3rd International Conference on Data Science and Information Technology, 2020Accurate forecasting of short-term load forecasting is of great help to demand-side response and power dispatching. In order to improve the accuracy of short-term power load prediction, the original power load data signals are decomposed by using the Variational Mode Decomposition (VMD) method. The decomposed sub-signals and the original signals form a
Han-Chieh Chao +4 more
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The Design and Realization of Power System Load Forecasting Software
2011Based on CC-2000 system with good openness, portability and scalability, we explain the structure and overall design of the load forecasting software, discuss how to apply meteorological information to load forecasting software steps and used instructions of weather software in detail, and finally point out the limitations of meteorological correction ...
Yan Yan +3 more
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Intelligent load forecasting techniques for local power suppliers
1999 European Control Conference (ECC), 1999This paper presents the results for daily load forecasts of a local power supplier. The new approach of this paper is to use three neural networks, each representing a period of a day, for the daily load forecast. Three commercial tools for neural networks are used to reduce the time for the development and tests.
B. Bitzer, Frank Rößer
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Study of Neural Networks for Electric Power Load Forecasting
2006Electric Power Load Forecasting is important for the economic and secure operation of power system, and highly accurate forecasting result leads to substantial savings in operating cost and increased reliability of power supply. Conventional load forecasting techniques, including time series methods and stochastic methods, are widely used by electric ...
Henry (Hui) Wang +4 more
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Power System Load Forecasting Based on EEMD and ANN
2011In order to fully mine the characteristics of load data and improve the accuracy of power system load forecasting, a load forecasting model based on Ensemble Empirical Mode Decomposition (EEMD) and Artificial Neural Networks (ANN) is proposed in this paper.
Wanlu Sun, Zhigang Liu 0001, Wenfan Li
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Power load forecasting using neural canonical correlates
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2002We (1998, 1999) have previously derived a neural network implementation of the statistical technique of canonical correlation analysis. We have then extended the network so that it may find nonlinear correlations in data sets. In this paper we demonstrate the capabilities of the network (both linear and nonlinear) on an artificial data set and ...
Pei Ling Lai +2 more
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