Earticle

초록

영어

The impact of actuators should be considered in the prediction modeling of greenhouse air temperature. In this paper, the operating state of a greenhouse was divided into five sub-states based on the on-off characteristic of actuators. A group of novel incremental auto regressive models with external variables (IARX models) suitable for the five operat-ing sub-states were deduced from the mechanistic modeling of greenhouse air tempera-ture. The new IARX models have fewer coefficients than other known ARX models. In or-der to validate the IARX prediction models, the related environmental factors of a glass greenhouse were measured. The prediction results of the IARX models were compared with two typical ARX models. The maximum prediction errors and the mean square errors of the IARX models, under the three operating sub-states of passive state(all actuators are not working), mechanical ventilation and fan-pad cooling, are 0.1°C, 0.14°C, 0.7°C, and 0°C, 0.3°C, 0.4°C, respectively. The prediction results are much better than those of one compared model, while similar with the other.

목차

Abstract
 1. Introduction
 2. Deduction of the IARX Models
 3. Measurement of Greenhouse Environmental Data
 4. Model Validation and Result Analysis
  4.1. Two Compared Models
  4.2. Model Identification and Validation
  4.3. Analysis of Results
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Zhenfeng Xu [ School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China ]
• Junjie Chen [ School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China ] Corresponding author
• Jingxia Zhang [ School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China ]
• Yuan Liu [ School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China ]
• Qiaowei Zhang [ School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605