Ultra-short-term wind speed and wind power forecast via selective Hankelization and low-rank tensor learning-based predictor

Abstract

Accurate wind power or wind speed point forecast (WPF/WSF) can provide useful information for decision-makers to achieve a better energy management. This paper proposes an efficient univariate forecasting framework with a selective Hankelization (SH) technique and a low-rank tensor learning-based predictor (LRP). SH is applied to transform the original time series (e.g., wind data×time point) into a high-order tensor structure (e.g., wind data×similarity×time point). In SH, Hankelization introduces the additional dimension; similarity search (SS) collects the training samples sharing most similar features to the test sample; similarity rearranger (SR) reorders the fibres/vectors in tensors. The above three steps construct the translation invariance features in tensors’ 2D slices. With the tensor structure, the forecasting task is transferred to a higher dimension, where the proposed LRP performs efficiently. It applies Tucker decomposition (TD) for low-rank approximation and extracts low-rank core tensors for regression, which reduces information redundancy and computational cost. Then the low-rank tensor learning network (LRN), which implements a long short-term memory network (LSTM) with attention as an encoder and a multilayer perceptron (MLP) as a decoder, is designed for tensors regression in LRP. Such encoder–decoder network is used as the tensor-to-tensor learning network and can fit the correlation between slices well. Finally, experiments are carried out using wind speed/power data obtained from two datasets. The results demonstrate that the proposed method, compared to the mainstream global forecasting methods, improves the NMAE, NRMSE, and MAPE criteria by 22%, 25%, and 19% for WPF and by 9%, 11%, and 8% for WSF. It also outperforms some state-of-the-art local forecasting methods in terms of accuracy, which improves the three criteria by 8%, 11%, and 7% for WPF and by 3%, 7%, and 3% for WSF. In this process, the mapping to high dimension, the use of SS, the strategy of multi-step sampling and the architecture of the LRN all play positive and effective roles in improving the accuracy.