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Very short-term load forecasting predicts the load over one hour into the future in five-minute steps and performs the moving forecast every five minutes. To quantify forecasting accuracy, the confidence interval is estimated in real-time. An effective prediction with a small associated confidence interval is important for area generation control and resource dispatch, and can help the operator further...
Very short-term load forecasting predicts load over one hour into the future in five minute steps and performs the moving forecast every five minutes. This is essential for area generation control and resource dispatch, and helps operators make good decisions. To quantify prediction accuracy, it is desirable to have a confidence interval for the forecasted load in real time. However, effective prediction...
In this paper, we propose a high order neural network (HONN) trained with an extended Kalman filter based algorithm to predict wind speed. Due to the chaotic behavior of the wind time series, it is not possible satisfactorily to apply the traditional forecasting techniques for time series; however, the results presented in this paper confirm that HONNs can very well capture the complexity underlying...
Power system dynamic state estimation (DSE) considers statistical characters of systemic state variables in past period, has functions of state estimation and forecasting, posses predominance that state estimation hasn't in terms of theory and practicability. On the basis of further study at DSE theory and method, a general framework for self-adapting dynamic estimator is presented here to improve...
The deregulation of electric power supply industries has raised many challenging problems. One of the most important ones is forecasting the Market Clearing Price (MCP) of electricity. Decisions on various issues, such as to buy or sell electricity and to offer a transaction to the market, require accurate knowledge of the MCP. Another problem, which has also been an important issue of the traditional...
In view of the dynamic nonlinear characteristics of power system loads, a short-term load forecasting (STLF) method for power system is proposed based on Wiener model, and Elman recursive neural network is used to fit in with its nonlinear part in this paper. Kalman filter is introduced to overcome the unknown disturbance in the linear part of the systems during loads prediction. Then, Elman neural...
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