A Novel Hybrid Collaborative Forecasting Method for Offshore Wind Power Based on Intelligent Optimization
DOI:
https://doi.org/10.4108/ew.12728Keywords:
Wind Power Forecasting, Data Cleaning, Modal Decomposition, Particle Swarm Optimization, Convolutional Neural NetworksAbstract
INTRODUCTION: With the rapid growth of offshore wind power integration and increasing wind power penetration, accurate power forecasting has become essential for maintaining grid stability and supporting economic dispatch.
OBJECTIVES: This paper aims to develop a high-accuracy offshore wind power forecasting framework that can effectively handle noisy, non-stationary data and reduce the impact of outliers on prediction performance.
METHODS: A two-stage data cleaning procedure is first constructed by combining density-based spatial clustering of applications with noise (DBSCAN) and polynomial regression to accurately identify and correct anomalous power data. The cleaned series is then decomposed using a sequential scheme that applies complete ensemble empirical mode decomposition followed by particle-swarm-optimized variational mode decomposition, producing multiple intrinsic mode components. Each component is fed into a hybrid temporal convolutional-gated recurrent unit (TCN-GRU) network, whose hyperparameters are globally tuned using an intelligent optimization algorithm, and the component-wise forecasts are aggregated to obtain the final power prediction.
RESULTS: Simulation studies based on measured data from an offshore wind farm show that the proposed method significantly reduces forecasting errors compared with conventional forecasting models and single-stage decomposition approaches.
CONCLUSION: The results demonstrate that the proposed adaptive optimization-based composite collaborative framework effectively improves both the accuracy and robustness of offshore wind power forecasting.
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Copyright (c) 2026 Ruanming Huang, Chen Qian, Tianli Song, Yumeng Jiang, Hongyu Wen, Bing Wang
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