Earticle

초록

영어

It researches the short-term electric load prediction and short-term electric load has the characteristics of time-varying, uncertainty, nonlinearity, etc., so the traditional linear prediction method cannot correctly describe the changing rule of the short-term electric load prediction, and neural network has the deficiencies including local minimum value of neural network, over-fitting and weak generalization ability, and the prediction accuracy is lower. In order to improve the accuracy of the short-term electric load prediction, this paper proposes a short-term electric load prediction model (IQPSO-BPNN) based on optimizing BP neural network. Firstly, it improves Quantum Particle Swarm Optimization to optimize the BP neural network parameters, and then adopts the optimized BP neural network to conduct modeling for the nonlinear change law of the short-term electric load prediction. Finally, it takes simulation test for the model performance. The simulation result shows that IPQPSO solves the problems of the BP neural network, and improve the prediction accuracy of the short-term electric load and reduce the prediction error.

목차

Abstract
 1. Introduction
 2. The Prediction Principle of Short-Term Load
 3. VPQPSO-BP Neural Network Short-Term Load Prediction Model
  3.1. BP Neural Network
  3.2. VPQPSO Algorithm
  3.3. Working Step of VPQPSO-BPNN Load Prediction Model
 4. Simulation Experiment
  4.1. Data Sources
  4.2. Comparison of Models and Evaluation Standards
  4.3. Data Pretreatment
  4.4. Results and Analysis
 5. Conclusion
 References

키워드

저자

• Tan Zhongfu [ School of Economics and Management, North China Electric Power University, Beijing, 102206, China ]
• Xin He [ School of Economics and Management, North China Electric Power University, Beijing, 102206, China ] Corresponding author
• Ju Liwei [ School of Economics and Management, North China Electric Power University, Beijing, 102206, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605