Earticle

초록

영어

Predicting the load in a building is essential for the optimal control of heating, ventilating and air-conditioning (HVAC) systems that use Ice Thermal Energy Storage (ITES) technology and also for cost and energy reduction of the non-storage systems. To solve the problems of the low accuracy of prediction by a single method, and most load predictions focusing on short-time prediction that cause reducing the practical significance, the application of the combined prediction method of time series and neural networks is presented in this paper. A case study shows that high accuracy is achieved by using the combined prediction model based on these two methods compared with the time series method in predicting the building load for longer time.

목차

Abstract
 1. Introduction
 2. Time Series Model
 3. Artificial Neural Networks (Ann) Model
 4. The Combined Model
 5. The Load Prediction by Using the Combined Model
  5.1. Removing Periodicity of Load
  5.2. Model Identification by Using Time Series Method
  5.3. TES Load Prediction Based On the Combined Model
 6. Conclusion
 References

키워드

저자

• Junhua Zhuang [ School of Chemical Engineering & Environment, Beijing Institute of Technology, Beijing 100081, China, School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China ]
• Yimin Chen [ School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China ]
• Xiaoxia Shi [ School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China ]
• Dong Wei [ School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605