• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of forecasting >On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks
【24h】

On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

机译：使用NARX神经网络预测长期电价成分在日前电价预测中的重要性

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

Daily and weekly seasonalities are always taken into account in day-ahead electricity price forecasting, but the long-term seasonal component has long been believed to add unnecessary complexity, and hence, most studies have ignored it. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, this framework is based on linear models estimated using ordinary least squares. This paper shows that considering non-linear autoregressive (NARX) neural network-type models with the same inputs as the corresponding SCAR-type models can lead to yet better performances. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can outperform the latter significantly. (C) 2017 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：在日前电价预测中始终考虑每日和每周的季节性因素，但长期以来长期的季节性因素被认为会增加不必要的复杂性，因此，大多数研究都忽略了这一点。最近引入的季节性分量自回归（SCAR）建模框架改变了这种观点。但是，此框架基于使用普通最小二乘法估计的线性模型。本文表明，考虑具有与相应SCAR类型模型相同的输入的非线性自回归（NARX）神经网络类型模型可以带来更好的性能。虽然单个季节成分人工神经网络（SCANN）模型通常比相应的SCAR类型结构差，但我们提供的经验证据表明SCANN网络的委员会机器可以明显优于后者。 （C）2017国际预报员协会。由Elsevier B.V.发布。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