Very short-term load forecasting: Multilevel wavelet neural networks with data pre-filtering

Very short term load forecasting predicts the load over one hour into the future in five minute steps, and is important in resource dispatch and area generation control. Effective forecasting, however, is difficult in view of noisy real-time data gathering and complicated features of load. This paper presents a method based on multilevel wavelet neural networks with novel pre-filtering. The key idea is to use a data pre-filtering method to detect and eliminate spikes within load, apply the wavelet technique to decompose the load into several frequency components, perform appropriate transformation on each component, and feed it together with other appropriate input to a separate neural network. Numerical testing demonstrates the significant value of data pre-filtering and multilevel wavelet neural networks, and shows that our method provides accurate forecasting.