Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM

Abstract

Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power. Temporal-spatial wind speed features contain rich information; however, their use to predict wind speed remains one of the most challenging and less studied areas. This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attention-based long short-term memory (LSTM), termed 2Attn-LSTM, a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data. To eliminate the unevenness of the original wind speed, we initially decompose the preprocessing data into IMF components by variational mode decomposition (VMD). Then, it encodes the spatial features of IMF components at the bottom of the model and decodes the temporal features to obtain each component's predicted value on the second layer. Finally, we obtain the ultimate prediction value after denormalization and superposition. We have performed extensive experiments for short-term predictions on real-world data, demonstrating that 2Attn-LSTM outperforms the four baseline methods. It is worth pointing out that the presented 2Atts-LSTM is a general model suitable for other spatial-temporal features.