...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Mathematical Modelling >Grey forecasting method of quarterly hydropower production in China based on a data grouping approach
【24h】

Grey forecasting method of quarterly hydropower production in China based on a data grouping approach

机译：基于数据分组法的中国水电季度产量灰色预测方法

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

Grey model GM (1,1) has been widely used in short-term prediction of energy production and consumption due to its advantages in data sets with small numbers of samples. However, the existing GM (1,1) modelling method can merely forecast the general trend of a time series but fails to identify and predicts the seasonal fluctuations. In the research, the authors propose a data grouping approach based grey modelling method DGGM (1,1) to predict quarterly hydropower production in China. Firstly, the proposed method is used to divide an entire quarterly time series into four groups, each of which contains only time series data within the same quarter. Afterwards, by using the new series of four quarters, models are established, each of which includes specific seasonal characteristics. Finally, according to the chronological order, the prediction results of four GM (1,1) models are combined into a complete quarterly time series to reflect seasonal differences. The mean absolute percent errors (MAPEs) of the test set 2011Q1-2015Q4 solved using the DGGM (1,1), traditional GM (1,1), and SARIMA models are 16.2%, 22.1%, and 22.2%, respectively; the results indicated that DGGM (1,1) has better adaptability and offers a higher prediction accuracy. It is predicted that China's hydropower production from 2016 to 2020 is supposed to maintain its seasonal growth with the third and first quarters showing the highest and lowest productions, respectively.
机译：灰色模型GM（1,1）由于其在具有少量样本的数据集中的优势，已广泛用于能源生产和能耗的短期预测。但是，现有的GM（1,1）建模方法只能预测时间序列的总体趋势，而无法识别和预测季节波动。在研究中，作者提出了一种基于数据分组方法的灰色建模方法DGGM（1,1）来预测中国的季度水力发电量。首先，所提出的方法用于将整个季度时间序列分为四个组，每个组仅包含同一季度内的时间序列数据。之后，使用新的四个季度系列，建立模型，每个模型都包含特定的季节性特征。最后，根据时间顺序，将四个GM（1,1）模型的预测结果合并为一个完整的季度时间序列，以反映季节差异。使用DGGM（1,1），传统GM（1,1）和SARIMA模型求解的测试集2011Q1-2015Q4的平均绝对百分比误差（MAPE）分别为16.2％，22.1％和22.2％；结果表明，DGGM（1,1）具有较好的适应性，并具有较高的预测精度。据预测，2016年至2020年中国的水力发电量将保持季节性增长，第三季度和第一季度分别显示最高和最低水平。

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