Wang Qi, Zhang Lei
School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu Province, China.
PeerJ Comput Sci. 2024 May 21;10:e2032. doi: 10.7717/peerj-cs.2032. eCollection 2024.
Aiming at the random and intermittent characteristics of wind speed, a short-term wind speed prediction (SWSP) method based on TSO-VMD-BiLSTM is proposed in this article. Firstly, open-source historical data from a certain region in 2022, including wind speed, direction, pressure, and temperature is analyzed. The data is processed through variational mode decomposition (VMD) to fully extract feature data from historical wind speed records. Secondly, taking historical wind speed, direction, pressure, and temperature as inputs and wind speed as output, a SWSP model based on long short-term memory (LSTM) network is constructed. Thirdly, the tuna swarm optimization (TSO) algorithm is utilized for parameters optimization, and a bi-directional long short-term memory (BiLSTM) network is incorporated to enhance prediction accuracy for micrometeorological parameters. The proposed TSO-VMD-BiLSTM model is validated through comparison with other models, demonstrating its higher accuracy with the maximum absolute error of only 2.52 m/s, the maximum root mean square error of 0.81, the maximum mean absolute error of only 0.54, and the maximum mean absolute percentage error of 6.89%.
针对风速的随机和间歇性特征，本文提出了一种基于TSO-VMD-BiLSTM的短期风速预测（SWSP）方法。首先，分析了某地区2022年的开源历史数据，包括风速、风向、气压和温度。通过变分模态分解（VMD）对数据进行处理，以充分提取历史风速记录中的特征数据。其次，以历史风速、风向、气压和温度为输入，风速为输出，构建了基于长短期记忆（LSTM）网络的SWSP模型。第三，利用金枪鱼群优化（TSO）算法进行参数优化，并引入双向长短期记忆（BiLSTM）网络以提高微气象参数的预测精度。通过与其他模型比较，验证了所提出的TSO-VMD-BiLSTM模型，结果表明其具有更高的精度，最大绝对误差仅为2.52m/s，最大均方根误差为0.81，最大平均绝对误差仅为0.54，最大平均绝对百分比误差为6.89%。
