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上海电力学院学报:2019,35(6):544-552,579
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基于深度学习的LSTM光伏预测
(上海电力学院 自动化工程学院)
Photovoltaic Prediction Research Based on Deep Learning LSTM
(School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)
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投稿时间:2018-11-26    
中文摘要: 随着对能源利用效率要求的提高及日益激增的光伏数据,传统的光伏预测方法已逐渐丧失优势。为了更加准确地进行光伏预测,采用深度学习的框架,并利用循环神经网络(RNN)中最重要的一个结构——长短时记忆网络(LSTM)对时间序列的强大处理能力进行了智能算法建模。由于LSTM具有"遗忘"与"更新"功能,很好地解决了长序依赖问题,从而使光伏预测在精度上有了质的变化,预测速度也得到显著提升。
Abstract:With the increasing demand for energy efficiency and the pohtovoltaic data that is increasing coustantly,traditional PV forecasting methods have gradually lost their advantages.In order to make more accurate PV predictions,a deep learning framework is adopted.Due to the powerful processing power of time series,the most important structure in the recurrent neural network (RNN)-long-short-term memory network (LSTM) is used for intelligent algorithm modeling.Because LSTM has the functions of "forgetting" and "updating",it solves the problem of long-order dependence,which makes the PV prediction have a qualitative change in accuracy,and the prediction speed is also significantly improved.
文章编号:     中图分类号:TM615    文献标志码:
基金项目:国家自然科学基金青年科学基金(51607111)。
引用文本:
崔承刚,邹宇航.基于深度学习的LSTM光伏预测[J].上海电力学院学报,2019,35(6):544-552,579.
CUI Chenggang,ZOU Yuhang.Photovoltaic Prediction Research Based on Deep Learning LSTM[J].Journal of Shanghai University of Electric Power,2019,35(6):544-552,579.