WPMamba: Enhanced Wind Power Forecasting Model Based on Mamba with Weather Forecast Data

As global warming intensifies, the development of wind energy becomes increasingly important. Given the inherent unpredictability of wind, there is a growing demand for sophisticated forecasting techniques to better incorporate wind energy into the existing power grid. Previous studies mostly relied on historical data to predict power generation without considering future climate conditions. These methods were only effective for short-term predictions, not for long-term predictions. To address the aforementioned issues, future weather forecast information is incorporated into the traditional framework within the paper. According to the new framework, a novel network model named WPMamba is proposed for predicting the output power of wind farm based on the Mamba model. Concurrently, a data preprocessing method, specifically tailored for wind power forecasting (WPF), is also introduced in this paper. Experiments show that the proposed WPMamba model demonstrates convincing performance in short-term, medium-term and long-term WPF tasks, achieving significant performance advantages over existing models. The ablation experiments also demonstrate that the introduction of future weather forecast information and our proposed data processing method can both enhance the model's forecasting accuracy.