Enhancing Wind Speed Prediction for Sustainable Energy Using Neural Networks

By Soham NandiReviewed by Susha Cheriyedath, M.Sc.Jan 11 2024

In an article published in the journal Nature, researchers focused on improving wind speed prediction for wind energy generation using an enhanced Hilbert–Huang transform (HHT) with complementary ensemble empirical mode decomposition (CEEMD). They employed a dynamic neural network model to optimize time series modeling, aiming for more accurate wind speed predictions.

Background

Wind energy has emerged as a vital renewable resource globally, with substantial growth in installed capacity. However, challenges in wind power generation, especially concerning the intermittent and unpredictable nature of wind speeds, necessitate accurate forecasting for grid stability and operational safety. Previous wind speed prediction models include physical methods relying on numerical weather prediction, statistical methods like autoregressive integrated moving averages (ARIMA), and artificial intelligence-based approaches, each with its limitations.

The present study addressed gaps in existing models by proposing an advanced wind speed prediction model using an improved HHT with CEEMD. The traditional empirical mode decomposition (EMD) method's limitations, such as component mode mixing and white noise interference, were overcome. The authors introduced a dynamic neural network model optimized with mathematical analytical methods, enhancing the accuracy of wind speed time series modeling.

The proposed methodology involved decomposing wind speed sequences using CEEMD, conducting spectral analysis on intrinsic mode functions (IMFs) through Hilbert transform (HT), and establishing neural network models for prediction based on spectral characteristics. The mathematical analytical model optimized weight coefficients for each IMF component, enabling a weighted summation for the final prediction. This approach was applied to wind-rich areas in Xinjiang, demonstrating significant improvements in reducing errors and enhancing predictive accuracy.

By introducing this optimized HHT - Nonlinear Autoregressive (NAR) model, the research not only contributed to theoretical innovation but also addressed practical challenges in wind speed prediction. The model's superior performance and generalizability made it an effective tool for sustainable wind energy utilization, bridging gaps in current forecasting methodologies and advancing the understanding of complex dynamics in wind power generation.

Improved HHT Model for Wind Speed Prediction

The HHT method, combining EMD and HT, decomposed complex time series into IMFs and a trend component. To address potential limitations of EMD, CEEMD was employed to eliminate mode mixing. The authors incorporated a NAR dynamic neural network for predictive modeling, utilizing previous outcomes for enhanced performance. A mathematical analytical model optimized the weights of IMFs, improving the accuracy of predictions. The proposed workflow involved preprocessing through CEEMD and HHT, followed by mathematical analysis for optimal weighting coefficients.

Subsequently, a NAR dynamic neural network was trained and optimized, with the final model exhibiting improved adaptability to complex wind speed data. The refined model, post-optimization, demonstrated superior accuracy in short-term wind speed predictions, addressing challenges posed by the stochastic nature of wind speeds on power grid stability. The researchers emphasized the significance of considering nonlinear and non-stationary features in wind speed data for precise predictions, enhancing the model's reliability across diverse wind speed fluctuations. This optimized HHT-NAR model offered a robust tool for the sustainable utilization of wind energy resources, contributing to the effective scheduling and control of wind farms while ensuring the stability of power systems.

Calculation and Analysis of Results

Wind speed prediction models were applied and evaluated using data from the Xinjiang Alashankou wind area and the Karamay wind farm. The analysis focused on an optimized HHT-NAR model, incorporating CEEMD for data preprocessing. Three evaluation metrics—root mean square Error (RMSE), coefficient of determination (R2), and mean absolute error (MAE)—were utilized to assess model performance. For the Alashankou wind area, the CEEMD decomposition revealed components with varying temporal characteristics and frequencies. The Pearson correlation coefficients were employed to assess the correlation between these components and the original wind speed data.

The optimal NAR dynamic neural network model was established for each component, and their predictions were summed to obtain the overall wind speed prediction. The optimized HHT-NAR model demonstrated improved smoothness and reduced volatility in wind speed time series. Evaluation metrics indicated a significantly enhanced predictive accuracy, with reduced RMSE and MAE, and an increased R2 value compared to other models, including long short-term memory LSTM and HHT-NAR. The model's universality was further tested in the wind energy sub-rich area of Karamay, showing superior predictive performance compared to other models. The optimized HHT-NAR model exhibited lower prediction errors and higher R2 values, validating its accuracy and effectiveness across different wind speed sequences.

Conclusion

In conclusion, this study effectively addressed wind speed intermittency and volatility by employing the CEEMD algorithm for decomposition and integrating it with the HHT method. The resulting optimized HHT-NAR dynamic neural network model demonstrated remarkable success in wind speed prediction at Xinjiang sites. The model showcased superior fitting accuracy, reducing RMSE and MAE in both wind-rich and wind-limited areas. Future work involves refining the model for optimal precision, conducting more extensive comparative studies, and exploring advanced algorithms. This research enhanced the reliability of wind speed predictions, thereby supporting the sustainable development of renewable energy.