• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >Provincial Grid Investment Scale Forecasting Based on MLR and RBF Neural Network
【24h】

Provincial Grid Investment Scale Forecasting Based on MLR and RBF Neural Network

机译：基于MLR和RBF神经网络的省级电网投资规模预测

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

Accurate calculation of power grid investment scale is an important work of power grid management. It is very important to power grid efficient development. Due to the characteristics of short data time series, lots of influencing factors, and large change of power grid investment, it is very difficult to calculate grid investment accurately. Firstly, this paper uses hierarchical clustering analysis method to divide the 23 provinces into four classes with considering fifteen power grid influencing factors, then uses spearman's rank-order correlation to find out five key influencing factors, and then establishes the regression relationship between the growth rate of investment scale and GDP, permanent population, total social electricity consumption, installed power capacity of operation area, maximum power load, and other growth rates by using the multiple linear regression method (MLR), and the estimation error is corrected by using RBF neural network. Finally, the validity of the model is verified by using data related to power grid investment. The calculation error indicates that the model is feasible and effective.
机译：准确计算电网投资规模是电网管理的重要工作。电网的高效发展非常重要。由于数据时间序列短，影响因素多，电网投资变化大等特点，很难准确计算电网投资。本文首先采用层次聚类分析法，在考虑15个电网影响因素的基础上，将23个省分为4类，然后利用Spearman的秩次相关性找出5个关键影响因素，然后建立增长率之间的回归关系。多元线性回归方法（MLR）对投资规模和GDP，常住人口，总社会用电量，运营区域装机容量，最大电力负荷和其他增长率的估计，并使用RBF神经校正估计误差网络。最后，通过使用与电网投资相关的数据来验证模型的有效性。计算误差表明该模型是可行和有效的。

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