Cluster-Driven Block-Flip Transformer With Dynamic Positional Learning for IoT-Based Wind Power Forecasting

In Internet of Things (IoT)-enabled energy systems, accurate wind power forecasting is essential for intelligent scheduling and grid stability. However, meteorological and power time series often exhibit time-varying distributions and irregular regime shifts. This complexity complicates stable pattern learning and may cause models to overemphasize repeated local patterns. To address this issue, we propose a Cluster-Driven Block-Flip Transformer with Dynamic Positional Learning (CBFT) for wind power forecasting in IoT-based energy applications. CBFT includes two components. First, a block-based clustering and flipping module splits the input into fixed-length blocks and clusters them by statistical similarity. It then applies controlled randomized flipping to selected blocks as structured regularization to reduce order-sensitive dependencies. Second, a dynamic positional learning module uses multi-layer perceptron (MLP) embeddings to learn adaptive relative positional representations among blocks. This design remains effective under block-level order perturbations. Comprehensive evaluations on multiple real-world wind power and electricity datasets show that CBFT achieves lower MAE and MSE than state-of-the-art Transformer-based models.