Application of Artificial Intelligence in the Field of Energy Conservation and Emission Reduction in Residential Air Conditioners

Shao Junqiang; Zheng Yulin; Che Mingjiang; Shi Yabo; Zhang Xiaobo; Yin Zhixin; Gao Neng; Liu Zhangqing; Wu Zhongyue; Xu Xiangguo

doi:10.3969/j.issn.0253-4339.2023.03.014

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Application of Artificial Intelligence in the Field of Energy Conservation and Emission Reduction in Residential Air Conditioners

更新时间：2024-07-18

- Application of Artificial Intelligence in the Field of Energy Conservation and Emission Reduction in Residential Air Conditioners
- Journal of Refrigeration Vol. 44, Issue 3, Pages: 14-28+40(2023)
- 作者机构：
  
  1. 浙江大学制冷与低温研究所浙江省制冷与低温技术重点实验室
  2. 浙大宁波理工学院
  3. 浙江大学平衡建筑研究中心
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0253-4339.2023.03.014
  CLC：
- Published：2023
- 稿件说明：
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Shao Junqiang, Zheng Yulin, Che Mingjiang, et al. Application of Artificial Intelligence in the Field of Energy Conservation and Emission Reduction in Residential Air Conditioners[J]. Journal of Refrigeration, 2023, 44(3): 14-28+40.
DOI：

Shao Junqiang, Zheng Yulin, Che Mingjiang, et al. Application of Artificial Intelligence in the Field of Energy Conservation and Emission Reduction in Residential Air Conditioners[J]. Journal of Refrigeration, 2023, 44(3): 14-28+40. DOI： 10.3969/j.issn.0253-4339.2023.03.014.

摘要

家用空调的普及带来了能耗的快速增长，人工智能技术为家用空调节能减排的推进提供了强有力的工具。家用空调存在众多可能的节能减排路径，而人工智能技术更是种类繁多，目前业界尚未结合这两个方面进行系统化的梳理，本文尝试以家用空调节能减排的实现途径为脉络，在各个途径下，总结整理各种可用的人工智能技术，并对每种技术进行了简要介绍。由于大多数的人工智能技术均离不开数据的支持，因此本文在最后还介绍了通过联合仿真来高效获取训练数据的方法。

Abstract

The increase in the use of residential air conditioners in Chinese households has resulted in a rapid increase in energy consumption. The rapid development of artificial intelligence technology provides a powerful tool for advancement in this field. However

there are various potential energy conservation and emission reduction paths for residential air conditioners

in addition to a wide variety of artificial intelligence technologies

which have not yet been systematically defined by the industry in combination with these two aspects. This paper summarizes and classifies various available artificial intelligence technologies with respect to achieving energy conservation and emission reduction for residential air conditioners and provides brief descriptions of these technologies. Because most artificial intelligence technologies are inseparable from data support

this study also introduces a method of efficiently obtaining training data through joint simulation.

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Related Author

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Related Institution

School of Energy and Power Engineering， Huazhong University of Science and Technology

China Mobile Group Design Institute Co.， Ltd.

Department of Energy Resources Engineering， School of Mechanics and Engineering Science， Peking University

Department of Refrigeration and Cryogenic Engineering, Beijing University of Technology

School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture

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