Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

By Hui Liu, H. Tian, Di Pan, Yan-fei Li. 2013

331 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting (2019, methodology): “There have been many papers in this area in recent years, including model combining stacked autoen-coder (SAE) and backpropagation algorithm [24], model based on wavelets theory, wavelet packets, time series analysis and ANNs [ 25].”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “Then, we consider an advanced prediction method, which integrates WT with ANN to capture the existing nonlinearity in time series of wind speed.”
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015, methodology): “, data decomposition for simplifying the complex data, individual prediction for each decomposed mode, and ensemble prediction for final prediction result [18,20,21].”