A Wind Power Forecasting Method Based on Lightweight Representation Learning and Multivariate Feature Mixing

With the rapid development of renewable energy, wind power forecasting has become increasingly important in power system scheduling and management. However, the forecasting of wind power is subject to the complex influence of multiple variable features and their interrelationships, which poses challenges to traditional forecasting methods. As an effective feature extraction technique, representation learning can better capture complex feature relationships and improve forecasting performance. This paper proposes a two-stage forecasting framework based on lightweight representation learning and multivariate feature mixing. In the representation learning stage, the efficient spatial pyramid module is introduced to reconstruct the dilated convolution part of the original TS2Vec representation learning model to fuse multi-scale features and better improve the gridding effect caused by dilated convolution while significantly reducing the number of parameters in the representation learning model. In the feature mixing stage, TSMixer is used as the basic model to extract cross-dimensional interaction features through its multivariate linear mixing mechanism, and the SimAM lightweight attention mechanism is introduced to adaptively focus on the contribution of key time steps and optimize the allocation of forecasting weights. The experimental results conducted on actual wind farm datasets show that the model proposed in this paper significantly improves the accuracy of wind power forecasting, providing new ideas and methods for the field of wind power forecasting.