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Very short-term load forecasting predicts the loads in electric power system one hour into the future in 5-min steps in a moving window manner. To quantify forecasting accuracy in real-time, the prediction interval estimates should also be produced online. Effective predictions with good prediction intervals are important for resource dispatch and area generation control, and help power market participants make prudent decisions. We previously presented a two level wavelet neural network method based on back propagation without estimating prediction intervals. This paper extends the previous work by using hybrid Kalman filters to produce forecasting with prediction interval estimates online. Based on data analysis, a neural network trained by an extended Kalman filter is used for the low-low frequency component to capture the near-linear relationship between the input load component and the output measurement, while neural networks trained by unscented Kalman filters are used for low-high and high frequency components to capture their nonlinear relationships. The overall variance estimate is then derived and evaluated for prediction interval estimation. Testing results demonstrate the effectiveness of hybrid Kalman filters for capturing different features of load components, and the accuracy of the overall variance estimate derived based on a data set from ISO New England. [ABSTRACT FROM PUBLISHER]

Very short-term load forecasting predicts the loads 1 h into the future in 5-min steps in a moving window manner based on real-time data collected. Effective forecasting is important in area generation control and resource dispatch. It is however difficult in view of the noisy data collection process and complicated load features. This paper presents a method of wavelet neural networks with data pre-filtering. The key idea is to use a spike filtering technique to detect spikes in load data and correct them. Wavelet decomposition is then used to decompose the filtered loads into multiple components at different frequencies, separate neural networks are applied to capture the features of individual components, and results of neural networks are then combined to form the final forecasts. To perform moving forecasts, 12 dedicated wavelet neural networks are used based on test results. Numerical testing demonstrates the effects of data pre-filtering and the accuracy of wavelet neural networks based on a data set from ISO New England. [ABSTRACT FROM PUBLISHER]