A DBSCAN-Based Data Cleaning and TCN-BiLSTM-PRGO Hybrid Model for Wind Power Forecasting

Wind power forecasting is essential for improving renewable energy exploitation and maintaining power system stability. However, influenced by factors such as the velocity and orientation of the wind and atmospheric pressure, wind power exhibits strong variability and uncertainty. Moreover, raw data often contains missing values, shutdown periods, and anomalies, which can degrade forecasting performance. Aiming at solving these challenges, this study develops a wind power forecasting approach integrating data cleaning with a hybrid prediction model. In the preprocessing stage, correlation analysis is employed to select meteorological variables strongly associated with power output as input features, thereby reducing redundancy and improving model effectiveness. Subsequently, missing values and shutdown records are removed, and an improved DBSCAN method is applied to detect anomalous samples. These outliers are then corrected using least squares regression, enhancing data quality while preserving continuity. In the forecasting stage, a hybrid model integrating TCN, BiLSTM, and the Plant Root Growth Optimization (PRGO) algorithm is developed. Specifically, TCN serves to capture local temporal features, while BiLSTM extracts bidirectional temporal dependencies. The PRGO serves to globally optimize model architecture parameters and key hyperparameters, improving convergence efficiency and generalization performance. Experiments on real wind farm data demonstrate that the proposed TCN-BiLSTM-PRGO model consistently outperforms all baselines (TCN, LSTM, TCN-BiLSTM, TCN-Transformer, and TCN-BiLSTM-WOA) across 12 h, 24 h, and 48 h horizons. At 12 h, it achieves a mean R2 of 0.942, NMAE of 6.014%, and NRMSE of 7.539% over five runs, improving R2 by 0.008–0.123 and reducing NMAE by 0.37–4.57 percentage points compared to other models. It also attains the highest R2 at 24 h (0.791) and 48 h (0.833). Statistical significance (p < 0.05) and chronological split tests (R2 = 0.940) further confirm their robustness and generalization. The proposed method offers a reliable solution for high-precision wind power forecasting.