Wang Zhen, Ying Youwei, Kou Lei, Ke Wende, Wan Junhe, Yu Zhen, Liu Hailin, Zhang Fangfang
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266075, China.
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Sensors (Basel). 2024 Jan 11;24(2):0. doi: 10.3390/s24020444.
In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.
为实现海上风电场的经济调度与安全稳定，解决海上风电功率预测中强随机性和强时间相关性问题，本文提出一种主成分分析（PCA）、麻雀算法（SSA）、变分模态分解（VMD）和双向长短时记忆神经网络（BiLSTM）的组合模型。首先，利用主成分分析算法（PCA）筛选多元时间序列数据以降低数据维度。其次，应用经SSA算法优化的变分模态分解（VMD）将风电功率时间序列数据自适应分解为不同频率成分的集合，以消除原始数据中的噪声信号；在此基础上，通过集成SSA算法优化BiLSTM模型的超参数，得到最终功率预测值。最后，通过仿真实验进行验证；结果表明，本文提出的模型有效提高了预测精度，验证了预测模型的有效性。
