基于长短期记忆网络的风电功率超短期预测  

Short-term Prediction of Wind Power Generation Based on LSTM

作　　者：刘吉雄 许思为 邹瑞 LIU Jixiong;XU Siwei;ZOU Rui(Wuhan Computer Peripheral Equipment Research Institute,Wuhan 430050,China;School of Business,Hubei University,Wuhan 430062,China)

机构地区：[1]武汉电器科学研究所有限公司,武汉430050 [2]湖北大学商学院,武汉430062

出　　处：《科技和产业》2025年第8期39-44,共6页Science Technology and Industry

摘　　要：全球环境问题凸显,风力发电作为污染小的可再生能源得到重视,风力资源的波动性和间歇性给风电场输出功率预测带来困难,影响电力系统的调度和运行。为提高风电场发电功率的预测精度,充分考虑天气特征对风电功率输出的影响,通过对风电场实测数据建模和预测,对比分析了不同深度学习模型超短期预测的效果。结果显示,基于一种长短期记忆神经网络的多变量时间预测的方法,能够对风电功率进行有效的超短期预测,较其他深度学习模型拥有更高的预测精度和稳定性。As global environmental issues become more prominent,wind power,a low-pollution renewable energy source,has garnered attention.However,the variability and intermittency of wind resources pose challenges for predicting wind farm output,affecting power system scheduling and operation.To improve the accuracy of wind power forecasting,weather characteristics influencing power output must be fully considered.By modeling and predicting actual wind farm data,the effectiveness of different deep learning models for ultra-short-term forecasting was compared.The results show that a multivariate time prediction method based on a long short-term memory(LSTM)network effectively predicts wind power,achieving higher accuracy and stability than other deep learning models.

关 键 词：风电 功率预测 长短期记忆网络 超短期 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]