Wind speed multi-step prediction based on the comparison of wind characteristics and error correction: Focusing on periodic thermally-developed winds

Abstract

To leverage the abundant wind energy resources available in mountainous regions, an increasing number of wind power facilities and associated transportation infrastructure are being constructed in these areas. There are different types of intense winds in mountainous areas and the non-linear characteristics of the different intense winds vary significantly. The periodic thermally-developed winds are the commonly occurring type of intense wind. Based on the comparison of wind characteristics, this research proposes a prediction model for the periodic thermally-developed winds. The research utilizes the Bi-directional Long Short-Term Memory model(Bi-LSTM), which has been optimized by the Bayesian optimization(BO), to predict wind speed. Based on the relationship between wind field characteristics in periodic thermally-developed winds, a regression correction module is proposed using a joint probability model. Additionally, to address the volatility and uncertainty of wind speed, a Gaussian mixture model is employed for probability prediction. The experimental results demonstrate several findings: 1. The BO-Bi-LSTM model accurately and reliably predicts multi-step results for periodic thermally-developed winds in mountainous areas. 2. The proposed correction module, when combined with other models, yields improved wind speed accuracy compared to the uncorrected values. 3. The Gaussian mixture model produces probabilistic prediction results that consider both interval coverage and interval bandwidth simultaneously. 4. The proposed model can successfully predict periodic thermally-developed winds in mountainous areas for practical engineering purposes, resulting in reliable probabilistic prediction outcomes. It is of great significance for the utilization of wind energy resources and the driving safety of vehicles on ancillary structures.