• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of forecasting >Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting
【24h】

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

机译：矢量自回归模型中高维风电功率预测的在线自适应套索估计

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

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：交货时间长达数小时的风电预测对于电力系统和市场的最佳，经济运行至关重要。矢量自回归（VAR）是一个框架，通过考虑其时间序列中的时空依赖性，该框架非常适合同时预测多个风电场。套索惩罚产生稀疏模型，并且可以避免在高维设置中过度拟合大量系数。但是，VAR模型中的估计通常不会考虑时空风力发电动态的变化，该变化与诸如季节或风电场设置变化等因素有关。本文通过提出时间自适应套索估计器和一种有效的坐标下降算法来在线递归更新VAR模型参数来解决此问题。该方法显示出良好的能力，可以跟踪模拟数据的多元时间序列动态变化。此外，在两个案例研究中，它显示出比非自适应套索VAR和单变量自回归更好的预测性能。 （C）2018国际预报员学会。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