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Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner

Author

Listed:
  • Dasheng Lee

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Fu-Po Tsai

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

This study developed cloud-based artificial intelligence (AI) that could run AI programs in the cloud and control air conditioners remotely from home. AI programs in the cloud can be altered any time to provide good control performances without altering the control hardware. The air conditioner costs and prices can thus be reduced by the increasing energy efficiency. Cloud control increased energy efficiency through AI control based on two conditions: (1) a constant indoor cooling rate and (2) a fixed stable range of indoor temperature control. However, if the two conditions cannot be guaranteed or the cloud signals are lost, the original proportional-integral-differential (PID) control equipped in the air conditioner can be used to ensure that the air conditioner works stably. The split-type air conditioner tested in this study is ranked eighth among 1177 air conditioners sold in Taiwan according to public data. It has extremely high energy efficiency, and using AI to increase its energy efficiency was challenging. Thus, this study analyzed the literature of AI-assisted controls since 1995 and applied it to heating, ventilation, and air conditioning equipment. Two technologies with the highest energy saving efficiency, a fuzzy + PID and model-based predictive control (MPC), were chosen to be developed into two control methodologies of cloud-based AI. They were tested for whether they could improve air conditioning energy efficiency. Energy efficiency measurement involved an enthalpy differential test chamber. The two indices, namely the energy efficiency ratio (EER) and cooling season power factor (CSPF), were tested. The EER measurement is the total efficiency value obtained when testing the required electric power at the maximum cooling capacity under constantly controlled temperature and humidity. CSPF is the tested efficiency value under dynamic conditions from changing indoor and outdoor temperatures and humidity according to the climate conditions in Taiwan. By using the static energy efficiency index EER for evaluation, the fuzzy + PID control could not save energy, but MPC increased the EER value by 9.12%. By using the dynamic energy efficiency index CSPF for evaluation, the fuzzy + PID control could increase CSPF by 3.46%, and MPC could increase energy efficiency by 7.37%.

Suggested Citation

  • Dasheng Lee & Fu-Po Tsai, 2020. "Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner," Energies, MDPI, vol. 13(8), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2001-:d:347084
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    References listed on IDEAS

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    1. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    2. Karali, Nihan & Shah, Nihar & Park, Won Young & Khanna, Nina & Ding, Chao & Lin, Jiang & Zhou, Nan, 2020. "Improving the energy efficiency of room air conditioners in China: Costs and benefits," Applied Energy, Elsevier, vol. 258(C).
    3. Petri, Ioan & Li, Haijiang & Rezgui, Yacine & Chunfeng, Yang & Yuce, Baris & Jayan, Bejay, 2014. "A modular optimisation model for reducing energy consumption in large scale building facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 990-1002.
    4. Masatoshi Sakawa & Takeshi Matsui, 2015. "Heat load prediction in district heating and cooling systems through recurrent neural networks," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 23(3), pages 284-300.
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    Cited by:

    1. Justyna Łapińska & Iwona Escher & Joanna Górka & Agata Sudolska & Paweł Brzustewicz, 2021. "Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland," Energies, MDPI, vol. 14(7), pages 1-20, April.
    2. Dasheng Lee & Liyuan Chen, 2022. "Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects," Sustainability, MDPI, vol. 14(12), pages 1-82, June.
    3. Gwanggil Jeon, 2022. "Artificial Intelligence Approaches for Energies," Energies, MDPI, vol. 15(18), pages 1-3, September.
    4. Mario Pérez-Gomariz & Antonio López-Gómez & Fernando Cerdán-Cartagena, 2023. "Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review," Clean Technol., MDPI, vol. 5(1), pages 1-21, January.
    5. Guofu Luo & Tianxing Sun & Haoqi Wang & Hao Li & Jiaqi Wang & Zhuang Miao & Honglei Si & Fuliang Che & Gen Liu, 2023. "An Energy-Saving Regulation Framework of Central Air Conditioning Based on Cloud–Edge–Device Architecture," Sustainability, MDPI, vol. 15(3), pages 1-20, January.

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