• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IFAC PapersOnLine >Mathematical examination of structural changes in load forecasting models ?
【24h】

Mathematical examination of structural changes in load forecasting models ?

机译：负载预测模型结构变化的数学检查 ？

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Electric power utilities need accurate load forecast model for reliable and efficient planning and operation of the grid. For short-term load forecast such as day-ahead case, data-driven mathematical models have been traditionally studied with various methods such as linear regression, time-series analysis, support vector machines and artificial neural networks. With large amount of historic data used for training the models, experimental approaches have usually attempted to show incremental improvement in accuracy. In experience, however, it has been observed that improvement in accuracy is limited to around a few percent of errors, which we consider should be caused by certain reasons. This motivated us to develop a theory to quantify the amount of historic data necessary to achieve a certain level of accuracy. This paper addresses the issue of quantifying the trade-off between model data requirement and accuracy. Using results of an examination performed on data from an electric utility, we demonstrate a novel method and mathematical criteria to judge the trade-off.
机译：电力实用程序需要准确的负载预测模型，可用于栅格的可靠和高效的规划和运行。对于短期负载预测，例如日前案例，传统上已经采用了各种方法研究了数据驱动的数学模型，例如线性回归，时间序列分析，支持向量机和人工神经网络。凭借用于培训模型的大量历史数据，通常试图对准确性提高增量提高。然而，在经验中，已经观察到，准确性的提高限制为大约几个百分比的错误，我们认为应该是由某些原因引起的。这激励我们制定一个理论，以量化实现一定程度的准确性所需的历史数据量。本文解决了量化模型数据要求和准确性之间的权衡问题的问题。使用对电力实用程序的数据进行检查的结果，我们展示了判断权衡的新方法和数学标准。

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