article · Biomimetics
Climate change is altering global wind patterns, creating more frequent storms, hurricanes, and quiet periods that complicate wind power generation and predictability. To improve predictions, a forecasting model pairs a Recurrent Neural Network with a bio-inspired Dynamic Fitness Al-Biruni Earth Radius optimisation algorithm to analyse wind power data patterns. The model was evaluated against several established approaches, including systems based on Particle Swarm Optimisation, Grey Wolf Optimizer, Whale Optimization Algorithm, Fire Hawk Optimizer, and the standard Al-Biruni Earth Radius algorithm. Using multiple statistical metrics, including analysis of variance and Wilcoxon Signed-Rank tests, the combined recurrent network and dynamic fitness model outperformed the alternative techniques in accurately predicting wind power patterns.
Changing weather patterns increase the volatility of wind power generation, making renewable energy harder to integrate into power grids. Improving predictive algorithms helps power system operators better anticipate energy yield fluctuations caused by sudden shifts in weather, supporting more stable and reliable electricity supply as climate variability increases.
This work could eventually assist wind energy producers, grid operators, and power forecasting software providers seeking more accurate generation estimates. Based on the abstract, the research is at an early computational stage, focused on model formulation and benchmark metric comparisons against other algorithms rather than live industrial deployment or integration into commercial control platforms.
AI-generated from the published abstract. Always read the original work before citing.
Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson's correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results.
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DOI: 10.3390/biomimetics8030321
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