Earticle

초록

영어

The relationship between med-long term load forecasting and socio-economic indicators is very difficult to describe with an accurate mathematical model. The paper introduce data mining technology into the association analysis of China's electricity consumption growth, select many socio-economic indicators since 2000, constitute the relevant factors database, complement of a few missing data, and dig out a number of indicators closely related to the electricity consumption with cluster analysis, and the data of distortion indicators is corrected, thus, build a more scientific load forecasting model. Validate and test the correlation of electricity consumption and selected indicators by dynamic neural network time sequence tool. The results show that the prediction model has good convergence, and the effect is satisfactory.

목차

Abstract
 1. Introduction
 2. Relevant Work
  2.1. The Complement of Missing Data
  2.2. Data Normalization Methods
  2.3. Cluster Analysis
  2.4. Correction in Distortion of Data
  2.5. Dynamic Neural Networks
 3. Experimental Analysis and Results
  3.1. Data Collection and Complement
  3.2. Data Normalization
  3.3. Cluster Analysis
  3.4. Analysis and Correction of Data
  3.5. The NARX Recursive Network Prediction
 4. Conclusion
 References

키워드

저자

• Li Xiaofeng [ Department of Informatic Science, Heilongjiang International University, Harbin 150025, China ]
• Yang Chunshan [ Department of Computer Science and Technology, Cheng-Dong College of Northeast Agricultural University, Harbin 150025, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605