MRIformer: A multi-resolution interactive transformer for wind speed multi-step prediction

Wind speed prediction is crucial for managing energy consumption in wind farms. Traditional wind speed prediction techniques often overlook two essential characteristics of wind speed data: (a) the downsampled wind speed data can retain cyclic and trend information, which is valuable for the model. (b) Multi-resolution speed data exhibited distinct patterns, enabling the model to extract insights from various perspectives. Considering the above two characteristics, this paper presents a novel approach called the Multi-Resolution Interactive transformer (MRIformer), which consists of the ASI block and the MRI block. The ASI block utilizes two different attention mechanisms to extract temporal information and enhance interactive learning among subsequences while downsampling wind speed data. The MRI block utilizes a tree structure to stack multiple layers of ASI blocks, enabling the analysis of wind speed data at various resolutions. By incorporating residual connections and multi-head attention, the MRI block effectively fuses data with different resolutions. Comparative experiments on three real-world datasets led to the following conclusions. (a) MRIformer exceeded 14 state-of-the-art baselines on all datasets and achieved a performance improvement of over 7.5%. (b) The effectiveness of the designed structure is demonstrated through component replacement and ablation experiments.