基于SSA-BiLSTM和奇异谱分析的短期风电功率预测
Short-term Wind Power Prediction Based on SSA-BiLSTM and Singular Spectrum Analysis
杨仁峥 1黄艳国 1何烜1
作者信息
- 1. 江西理工大学电气工程与自动化学院,赣州 341000
- 折叠
摘要
针对风电功率序列具有波动性和较高复杂度的特点,提出了一种基于麻雀算法(sparrow search algorithm,SSA)优化的双向长短期记忆神经网络(bidirectional long and short-term memory neural network,BiLSTM)和奇异谱分析的短期风电功率预测模型.首先,采用奇异值分析对历史功率数据进行特征提取,去噪处理减少噪声信息干扰;其次,利用麻雀算法对BiLSTM模型超参数寻优,以BiLSTM为基础构建风电功率预测模型,提高了模型训练效率;最后,采用某风电场的运行数据验证模型精度并对比其他模型验证模型合理性.实验结果表明:改进后的模型相对于基准模型,绝对误差降低了 14.2％,均方根误差降低了 4.24％,本文所提改进BiLSTM模型具有较好的预测性能,能有效提高短期风电功率预测的精度.
Abstract
A short-term wind power prediction model based on SSA(sparrow search algorithm),optimized BiLSTM(bidirectional long and short-term memory neural network),and singular spectrum analysis was proposed in order to address the characteristics of wind power sequences with volatility and increasing complexity.First,features were extracted from historical power data using singular value analysis,and noise information interference was minimized using the denoising method.Second,the sparrow method was used to optimize the hyper-parameters of the BiLSTM model,and the wind power prediction model was constructed utilizing the BiLSTM,im-proving the effectiveness of model training.Finally,operational data from a wind farm was used to assess the model's accuracy and logic by comparing it to other models.Based on the experimental results,the improved model can effectively improve the accuracy of short-term wind power prediction,reducing the absolute error by 14.2％and the root-mean-square error by 4.24％relative to the baseline model.The improved BiLSTM model proposed in this paper has better prediction performance.
关键词
风电功率预测/双向长短期记忆神经网络(BiLSTM)/麻雀搜索算法/奇异谱分析Key words
wind power forecasting/BiLSTM(bidirectional long and short-term memory neural network)/sparrow search algo-rithm/singular spectrum analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据