Advanced Short-Term Wind Power Forecasting Based on CNN-BiLSTM - Lightweight Self-Attention (LWSA)

Abstract

Accurate short-term wind power forecasting is critical for maintaining grid stability and enhancing energy dispatch. However, the nonlinear, volatile, and uncertain nature of wind power poses significant challenges to traditional and deep learning models. To address this, a hybrid model named CNN-BiLSTM-LWSA is proposed, which integrates Convolutional Neural Networks (CNN) for local pattern extraction, Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal modeling, and a Lightweight Self-Attention (LWSA) mechanism based on Lin-former. The LWSA module applies low-rank projections to reduce attention complexity from O(n²) to O(n), enabling efficient long-sequence learning while preserving global dependencies. Experi-ments were conducted using a full-year dataset (35,040 records at 15-minute intervals) from the Mahuangshan First Wind Farm in Ningxia, China. The model was tested under various input win-dow lengths (1h, 3h, 12h, 24h, and 32h). Results show that CNN-BiLSTM-LWSA consistently out-performs CNN-BiLSTM and CNN-BiLSTM-Attention in both accuracy and efficiency. Under a 24-hour input, it achieves an RMSE of 53.4 kW, MAE of 23.2 kW, and R2 of 0.955 while reducing training and testing time by 54.8% and 47.1%, respectively, compared to the attention-based base-line. Even with a 32-hour input, the model maintains low prediction errors and stable R2, validating its scalability. The experimental results fully confirm that CNN-BiLSTM-LWSA effectively balances forecasting accuracy and computational cost across different temporal settings, offering a robust, efficient, and practical solution for short-term wind power forecasting applications.