Earticle

초록

영어

Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. The SVR model with Particle Swarm Optimization and Cross Validation is proposed according to the characteristics of the nonlinear electricity consumption data which are new Data Mining Techniques (DMT). In this model, PSO-CV method is used to the parameter determination. Then PSO-CV-SVR model is applied to the electricity consumption prediction of Jiangsu province. The result shows better than the ANNs method and improves the accuracy of the prediction.

목차

Abstract
 1. Introduction
 2. Principle of SVR
 3. Finding of Optimization by SVR with Particle Swarm
 4. Modeling and Prediction
  4.1. Data Choosing and Pre-disposing
  4.2. Result of Regression Forecasting of the Model
  4.3. Experiment Study
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Zeguo Qiu [ School of Computer and Information Engineering, Harbin University of Commerce Harbin Univ Commerce, Sch Comp & Informat Engn, Harbin 150028, Heilongjiang, Peoples R 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