Enhanced Short-Term Wind Power Prediction via an Innovative Multitask Learning Framework Integrated With Fusion Model-Based Data Cleansing

To enhance the training efficiency and prediction accuracy of multi-step short-term wind power prediction (WPP), and to tackle the challenge that data cleaning with only a single model fails to accurately detect complex outliers in wind power dataset, this study proposes a novel short-term WPP framework that integrates multi-task learning (MTL) with an advanced fusion data cleaning methodology (DCM). First, a hybrid machine learning model is designed for anomaly detection and correction. The Isolation Forest algorithm identifies global outliers in wind speed data, while a boundary-aware local density outlier detection algorithm captures local anomalies. Subsequently, the Density-Based Spatial Clustering of Applications with Noise algorithm uncovers abnormal wind speed-power generation relationships. Upon outlier detection, a backpropagation neural network performs anomaly correction, ensuring reconstructed data quality. The cleaned dataset then trains an MTL-based short-term multi-step WPP model. This framework partitions different time steps into distinct tasks and introduces Gaussian perturbations to historical power values during inter-task communication in training, mitigating discrepancies between training and prediction phases. A Decision Tree, Support Vector Machines and several shallow neural networks generate preliminary multi-step predictions, which are further refined by bidirectional long short-term memory (BiLSTM) network to produce final forecasts. Comprehensive numerical experiments validate the effectiveness of the proposed framework demonstrating that the proposed DCM achieves 90% anomaly detection with corrected data reconstruction quality reaching R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.95, and the MTL based WPP method outperforms existing benchmark approaches in forecasting accuracy across multiple evaluation metrics.