Earticle

초록

영어

In order to overcome the problems which are difficult to be accurately predicted, such as voilent vibration, large amplitude, and pseudoperiod, we put forward a load-classification method and a model-selection method following a multi-model merit. The multi-model merit can be realized by the result of Network training, so we can forecast the load of iron and steel enterprises respectively. In this way, we can avoid the limitations of traditional load forecasting methods which simply depend on sample datas. In the framework of this model, to minimize the load forecast error is the target. On the one hand, it can be convenient to add new models into the framework, so as to improve the accuracy of the prediction, find more characteristics of the load, and better the model. On the other hand, based on the load data, we can also adaptively change the way the model is formed, so as to expand the applicability of prediction methods. By simulating different load management forecasting systems, we confirm that the effectiveness of the proposed method is verified. A software package based on the methods presented in this paper for power systems scheduling is also completed. Some native steel generation corporations have already used the system.

목차

Abstract
 1. Introduction
 2. Selecting-best Multi-model Prediction Framework of Adaptive Data Quality
  2.1. Model space
  2.2 Combination of multi-model
  2.3 Frame design
 3. Multi-modeling and Simulation Analysis of Daily Load Forecasting
  3.1 Combination of moving average and linear regression model
  3.2 Artificial neural network [17]
  3.3 Support vector regression model
 4. Multi-model Selection
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Qingxin Zhang [ School of Automation, Shenyang Aerospace University, Shenyang 110136, China ]
• Zhanbo Cui [ School of Automation, Shenyang Aerospace University, Shenyang 110136, China ]
• Luping Wang [ School of Automation, Shenyang Aerospace University, Shenyang 110136, China ]
• Yanki Zhou [ School of Automation, Shenyang Aerospace University, Shenyang 110136, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605