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A predictive control strategy for electrochromic glazing to balance the visual and thermal environmental requirements: Approach and energy-saving potential assessment

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  • Sun, Yuying
  • Hao, Yingying
  • Wang, Dan
  • Wang, Wei
  • Deng, Shiming
  • Qi, Haoran
  • Xue, Peng

Abstract

Electrochromic (EC) glazing provides a promising solution to actively adjusting the sunlight and radiant heat through windows in response to building visual and thermal environmental requirements, and control strategies play a critical role in the application of EC technology in buildings. However, there is a conflict between the visual and thermal environmental requirements for EC regulation during a cooling season. To solve this problem, this paper developed a predictive control strategy for EC glazing. It was achieved based on two theoretical models which could predict the energy performance of EC glazing at different tint states, including the solar radiation heat transmitted through the glazing and lighting energy consumption. The optimum glazing state was determined based on real-time minimized energy consumption. Study results showed that the predictive control strategy outperformed the rule-based control strategy, with percentage energy saving of an office room in Beijing, China from 8.04% to 12.78% over an entire cooling season. In addition, the paper evaluated the energy-saving of using EC glazing under the predictive control strategy in 28 Chinese cities. The results indicated that EC glazing was highly applicable in most of cities with energy saving from 2.35 kWh/m2 to 5.04 kWh/m2 over an entire cooling season.

Suggested Citation

  • Sun, Yuying & Hao, Yingying & Wang, Dan & Wang, Wei & Deng, Shiming & Qi, Haoran & Xue, Peng, 2022. "A predictive control strategy for electrochromic glazing to balance the visual and thermal environmental requirements: Approach and energy-saving potential assessment," Renewable Energy, Elsevier, vol. 194(C), pages 334-348.
    • Handle: RePEc:eee:renene:v:194:y:2022:i:c:p:334-348
    DOI: 10.1016/j.renene.2022.05.093
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    References listed on IDEAS

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    Cited by:

    1. Shi, Shaohang & Zhu, Ning & Wu, Shuangdui & Song, Yehao, 2024. "Evaluation and analysis of transmitted daylight color quality for different colored semi-transparent PV glazing," Renewable Energy, Elsevier, vol. 222(C).
    2. Ouyang, Tiancheng & Zhang, Mingliang & Qin, Peijia & Tan, Xianlin, 2024. "Flow battery energy storage system for microgrid peak shaving based on predictive control algorithm," Applied Energy, Elsevier, vol. 356(C).
    3. Sun, Yuying & Li, Yunhe & Xu, Wenjing & Wang, Wei & Wei, Wenzhe & Zhang, Chunxiao, 2023. "A glare predictive control strategy for split-pane electrochromic windows: Visual comfort and energy-saving assessment," Renewable Energy, Elsevier, vol. 218(C).

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