Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network

Abstract

The intermittent nature of wind can represent an obstacle to get reliable wind speed forecasting, thus many methods were developed to improve the accuracy, due to unstable behavior patterns and the presence of noise signal. In order to overcome this issue, a preprocessing step is desirable to provide more reliable data. Decomposition strategy is reported as the crucial component of this improving task of the wind speed forecasting. It can be applied as the first step or as a recurrent process, and normally the raw wind speed data is decomposed in several signal patterns. Based on this understanding, this paper proposed a combination of two signal decomposition strategies, known as variational mode decomposition (VMD) and singular spectral analysis (SSA), with modulation signal theory. The proposed decomposition approach is further coupled with a long short-term memory neural network (LSTM), the adaptive neuro-fuzzy system (ANFIS), echo state network (ESN), support vector regression (SVR) and Gaussian regression process (GRP) models resulting in new ensemble learning approaches. All results obtained through these ensembles are compared between them and demonstrated an error stabilization behavior, ability decomposing the wind speed into uncorrelated components, reducing the errors from one up to twelve steps-ahead forecasting. In general terms, the results indicate that ensembles learning framework are robust and reliable to applications in wind speed forecasting task.