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Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering

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5 Author(s)
Che Guan ; Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA ; Luh, P.B. ; Michel, L.D. ; Yuting Wang
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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.

Published in:

Power Systems, IEEE Transactions on  (Volume:28 ,  Issue: 1 )