Peak interval-focused wind power forecast with dynamic ramp considerations

Abstract

Wind power forecast (WPF) plays a crucial role in the reliable and safe operation of power systems. Existing wind power prediction methods mainly focus on the overall trend information, often neglecting the performance of some typical intervals that can significantly impact the reserve requirements of power system, e.g., unexpected ramp-up and ramp-down, local peak interval (LPI), and so on. Therefore, the primary focus of this paper is to explore techniques for enhancing the accuracy of wind power during the LPI. Considering that the points during the LPI are influenced by dynamic ramp characteristics, with the steepness and magnitude of ramps influencing the range of LPI, this paper proposes a novel focused-LPI model by incorporating dynamic ramp considerations. Specifically, beyond the tradition forecasting model, the proposed model encodes the information of ramp occurrence in wind power data based on a positional encoder; subsequently, the proposed model not only outputs standard predictions within a prediction range but also identifies the presence of ramps within these predictions. In addition, this paper employs a dynamically weighted loss function to optimize the proposed multi-task model. The proposed model is applied for various network architectures, and the results confirm that the proposed model is widely suitable for various basis network architectures. Specifically, the R-squared (R2) of the proposed model for the overall trend can exist a little error compared to the initial basic network architectures; the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Relative Error (MRE) of the proposed model for predicting the LPI points can outperform the initial basis network architecture by average enhancements of 7.10%, 2.66%, and 3.46%, respectively. In addition, compared to existing state-of-the-art (SoTA) models in the LPI points, the proposed model obtains improved performance with 14.43% for MAE and 11.19% for RMSE, respectively, presenting its substantial performance enhancement.

Keywords

• Wind power forecast
• Local peak interval
• Wind power ramps
• Deep learning