基于改进LSTM神经网络的风电功率短期预报算法

A Short-Term Calgorithm Based on Improved LSTM Neural Network

[目的]风能的波动性和间歇性给大规模的风电并网提出了挑战,解决这一问题的有效途径是能够提供准确的风电功率预报。[方法]针对这一挑战,提出了一种新的基于改进LSTM(长短期记忆)架构的深度学习神经网络的风功率预报模型,包含自主研制的数据异常检测与处理、风速特征提取、超参数调优于一体的风电功率预报方法。为了使神经网络模型能更加准确地学习风速特征对风电功率的影响,还定义了一种使用特征筛选以及特征倍增相结合的特征工程方法。[结果]仿真结果表明:所提出的数据清洗及数据增强算法在各种机器学习算法上可以将准确率提升约5%。提出的改进LSTM神经网络模型在数据清洗后与传统算法以及业内主流神经网络算法进行对比,可以将准确率提升2.5%。[结论]改进的方法不但具有较好的噪声数据清洗能力,而且在所有的试验中,改进模型在预报准确性方面优于其他所有算法,可以为实际应用提供指导。

[Introduction]The volatility and intermittency of wind energy pose significant challenges for large-scale wind power integration.An effective approach to address this issue is to provide accurate wind power forecasting.[Method]In response to this challenge,this study proposed a wind power forecasting model for deep learning neural networks based on an improved LSTM(Long Short-Term Memory)architecture.The model incorporated a wind power forecasting approach that included independently developed data anomaly detection and processing,wind speed feature extraction and hyperparameter tuning.To enhance the neural network model's ability to accurately learn the impact of wind speed features on wind power,a feature engineering method combining feature screening and feature augmentation was also defined.[Result]The simulation results demonstrate that the proposed data cleaning and data augmentation algorithm can enhance the accuracy of various machine learning algorithms by approximately 5%.Furthermore,the proposed improved LSTM neural network model,after data cleaning,outperforms traditional algorithms and state-of-the-art neural network algorithms in the industry,achieving a 2.5%increase in accuracy.[Conclusion]The improved approach not only exhibits robust capability in cleaning noisy data but also consistently outperforms other algorithms in terms of forecasting accuracy across all experiments.This model provides valuable guidance for practical applications in the field of wind power forecasting.