Short-Term Wind Power Forecast Based on Continuous Conditional Random Field

Li, Menglin; Yang, Ming; Yu, Yixiao; Wu, Qiuwei

doi:10.1109/tpwrs.2023.3270662

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…Meanwhile, [4] presents a novel 3D model that leverages solar-assisted air velocity conversion to enhance wind energy generation, conducting a numerical analysis using sulting in superior performance over benchmarks. By introducing a novel Cross-Correlation Recurrent Framework (CCRF) model, this research combines Bidirectional Long Short-Term Memory (Bi-LSTM) and gaussian kernels for wind power forecasting, thus outperforming benchmark models through the consideration of both temporal autocorrelation and Numerical Weather Prediction (NWP) correlation [27].…”

Section: Introductionmentioning

confidence: 99%

DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm

Safari,

Kheirandish Gharehbagh,

Nazari Heris

2023

Energies

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The transition to sustainable electricity generation depends heavily on renewable energy sources, particularly wind power. Making precise forecasts, which calls for clever predictive controllers, is a crucial aspect of maximizing the efficiency of wind turbines. This study presents DeepVELOX, a new methodology. With this method, sophisticated machine learning methods are smoothly incorporated into wind power systems. The Increased Velocity (IN-VELOX) wind turbine framework combines the Gradient Boosting Regressor (GBR) with the Grey Wolf Optimization (GWO) algorithm. Predictive capabilities are entering a new age thanks to this integration. This research presents DeepVELOX, its structure, and results. In particular, this study presents the considerable performance of DeepVELOX. With a MAPE of 0.0002 and an RMSPE of 0.0974, it gets outstanding Key Performance Indicator (KPI) results. The criteria of Accuracy, F1-Score, R2-Score, Precision, and Recall, with a value of 1, further emphasize its performance. The result of this process is an MSE of 0.0352. The significant reduction in forecast disparities is made possible by this system’s remarkable accuracy. Along with improving accuracy, the integration of machine learning algorithms, including GBR, the GWO algorithm, and wind turbine operations, offer a dynamic framework for maximizing power and energy capture.

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Section: Introductionmentioning

confidence: 99%