...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Renewable energy >PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information
【24h】

PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information

机译：在没有未来气象信息的情况下，使用反复性神经网络的峰值区的PV功率预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As the majority of daily PV power outputs is mostly obtained in a peak zone around noon, hourly PV power output prediction in a peak zone is considered as an essential function for more sophisticated operations of PV facilities. However, the prediction of PV power output in a peak zone is a challenging problem since meteorological information is continuously changing and difficult to obtain for a particular area. In addition, due to only using the meteorological information observed in the morning to estimate PV power outputs around noon, the input features which are utilized as a shorter horizon from the horizon of the prediction are making the problem even more complex. Therefore, this study proposes two PV power output prediction model by using long short-term memory (LSTM) and gate recurrent network (GRU). In particular, unlike the previous methods, the proposed models attempt to understand the hidden sequential patterns of PV power outputs based only on the information captured in the morning without utilizing future meteorological information observed around noon during training. The experiment results using a real-world dataset indicate that the proposed models perform better PV power prediction in the peak zone than conventional models.& nbsp; (c) 2020 Elsevier Ltd. All rights reserved.
机译：由于每日PV功率输出大多数在中午的峰值区中获得，峰值区域中的每小时PV功率输出预测被认为是PV设施更复杂操作的基本函数。然而，由于气象信息连续变化并且难以获得特定区域，因此峰值区域中的PV功率输出的预测是一个具有挑战性的问题。另外，由于仅使用早上观察到的气象信息来估计中午的光伏电量，因此从预测的地平线上使用作为较短地平线的输入特征使得问题更加复杂。因此，本研究通过使用长短期存储器（LSTM）和栅极复制网络（GRU）提出了两个PV功率输出预测模型。特别地，与先前的方法不同，所提出的模型试图仅基于清晨捕获的信息的光伏电力输出的隐藏顺序模式，而不利用在训练期间在中午左右观察到的未来气象信息。使用实际数据集的实验结果表明所提出的型号比传统模型在峰值区域中执行更好的PV功率预测。＆NBSP; （c）2020 elestvier有限公司保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号