A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting (WPF) accuracy. According to theory of direct and recursive multi-step prediction, this study firstly proposes the models of R (recursive)-VMD (variational model decomposition)-LSTM (long short-term memory) and D (direct)-VMD-LSTM for the hourly forecast of day-ahead wind power by using a combination of a novel and in-depth neural network forecasting model called LSTM and the variational model decomposition (VMD) technique. The data from these model tests were obtained from two real-world wind power series from a wind farm located in Henan, China. The experimental results show that LSTM can achieve more precise predictions than traditional neural networks, and that VMD has a good self-adaptive ability to remove the stochastic volatility and retain more adequate data information than empirical mode decomposition (EMD). Secondly, the R-VMD-LSTM and D-VMD-LSTM are comparatively studied to analyze the accuracy of each step. The results verify the effectiveness of the combination of the two models: The R-VMD-LSTM model provides a more accurate prediction at the beginning of a day, while the D-VMD-LSTM model provides a more accurate prediction at the end of a day.

In the development of data-driven models for streamflow forecasting, choosing appropriate input variables is crucial. Although random forest (RF) has been successfully applied to streamflow forecasting for input variable selection (IVS), comparative analysis of different random forest-based IVS (RF-IVS) methods is yet absent. Here, we investigate performance of five RF-IVS methods in four data-driven models (RF, support vector regression (SVR), Gaussian process regression (GP), and long short-term memory (LSTM)). A case study is implemented in the contiguous United States for one-month-ahead streamflow forecasting. Results indicate that RF-IVS methods enable to acquire enhanced performance in comparison to widely used partial Pearson correlation and conditional mutual information. Meanwhile, performance-based RF-IVS methods appear to be superior to test-based methods, and the test-based methods tend to select redundant variables. The RF with a forward selection strategy is finally recommended to connect with GP model as a promising combination having potential to yield favorable performance.