article · PLoS ONE
Accurate wind speed forecasting is essential for maintaining the safety and stability of power networks that integrate significant wind energy, yet the natural variability of wind makes this challenging. A new forecasting approach uses a weighted ensemble model optimised by an adaptive dynamic grey wolf-dipper throated optimisation algorithm. This meta-heuristic algorithm combines group cooperation mechanisms with bird-hunting dynamics to balance exploration and exploitation during feature selection and hyperparameter tuning. It was applied to optimise multi-layer perceptron, K-nearest regressor, and long short-term memory regression models. Tested on benchmark data from the Global Energy Forecasting Competition 2012, the ensemble model achieved a root mean square error of 0.0035, outperforming existing state-of-the-art methods. Statistical tests, including analysis of variance and Wilcoxon rank-sum tests, confirmed the stability and robustness of the forecasting framework.
Power networks with high levels of wind energy penetration require precise wind speed forecasts to maintain operational safety and grid balance. Because wind speed is erratic and difficult to model, enhanced machine learning techniques that improve predictive accuracy can help power system operators anticipate fluctuations, manage reserve generation, and integrate renewable power more reliably into electricity grids.
The method could enable improved forecasting software for electricity grid operators and wind farm managers needing to manage energy dispatch and power grid stability. While the technique demonstrates high predictive accuracy and statistical robustness on competitive benchmark data from 2012, it remains at the applied testing stage on historical datasets, requiring integration into real-time operational management systems before commercial deployment.
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Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature's state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm's stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum.
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DOI: 10.1371/journal.pone.0278491
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