• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Intelligent distributed computing VIII >A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models
【24h】

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models

机译：基于数值天气模型的每日太阳能预报的机器学习技术研究

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

Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach. We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.
机译：在可再生能源的背景下，预测太阳能已成为一个重要问题，并且机器学习算法在该领域起着重要的作用。可以使用历史数据将太阳能的预测作为时间序列预测问题解决。同样，可以从数值天气预报模型（NWP）中得出太阳能预报。我们的兴趣集中在后一种方法上。我们专注于通过GEFS（全球整体预报系统）计算的NWP预测太阳能的问题，该系统可预测网格中各点的气象变量。在这种情况下，了解如何根据用作机器学习技术输入的网格节点数量来提高预测精度可能很有用。但是，使用来自大量网格节点的变量可能会导致许多属性，这可能会降低学习算法的泛化性能。本文使用Kaggle提供的俄克拉荷马州的数据，通过比较支持向量机和梯度增强回归，研究了这两个问题。此外，还测试了三种不同的特征选择方法：线性相关，ReliefF算法以及基于局部信息分析的新方法。

著录项

  • 来源
  • 会议地点 Madrid(ES)
  • 作者

    Ricardo Aler; Ricardo Martin; Jose M. Valls; Ines M. Galvan;

  • 作者单位

    Computer Science Department, Carlos Ⅲ University, Spain;

    Computer Science Department, Carlos Ⅲ University, Spain;

    Computer Science Department, Carlos Ⅲ University, Spain;

    Computer Science Department, Carlos Ⅲ University, Spain;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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