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A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand

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  • Tien, Paige Wenbin
  • Wei, Shuangyu
  • Liu, Tianshu
  • Calautit, John
  • Darkwa, Jo
  • Wood, Christopher

Abstract

Building ventilation accounts for up to 30% of the heat loss in commercial buildings and 25% in industrial buildings. To effectively aid the reduction of energy consumption in the building sector, the development of demand-driven control systems for heating ventilation and air-conditioning (HVAC) is necessary. In countries with temperate climates such as the UK, many buildings depend on natural ventilation strategies such as openable windows, which are useful for reducing overheating prevalence during the summer. The manual opening and adjustment of windows by occupants, particularly during the heating season, can lead to substantial heat loss and consequent energy consumption. This could also result in the unnecessary or over ventilation of the space, or the fresh air is more than what is required to ensure adequate air quality. Furthermore, energy losses build up when windows are left open for extended periods. Hence, it is important to develop control strategies that can detect and recognise the period and amount of window opening in real-time and at the same time adjust the HVAC systems to minimise energy wastage and maintain indoor environment quality and thermal comfort. This paper presents a vision-based deep learning framework for the detection and recognition of manual window operation in buildings. A trained deep learning model is deployed into an artificial intelligence-powered camera. To assess the proposed strategy's capabilities, building energy simulation was used with various operation profiles of the opening of the windows based on various scenarios. Initial experimental tests were conducted within a university lecture room with a south-facing window. Deep learning influenced profile (DLIP) was generated via the framework, which uses real-time window detection and recognition data. The generated DLIP were compared with the actual observations, and the initial detection results showed that the method was capable of identifying windows that were opened and had an average accuracy of 97.29%. The results for the three scenarios showed that the proposed strategy could potentially be used to help adjust the HVAC setpoint or alert the occupants or building managers to prevent unnecessary heating demand. Further developments include enhancing the framework ability to detect multiple window opening types and sizes and the detection accuracy by optimising the model.

Suggested Citation

  • Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
    • Handle: RePEc:eee:renene:v:177:y:2021:i:c:p:603-625
    DOI: 10.1016/j.renene.2021.05.155
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    References listed on IDEAS

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    1. Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.
    2. Allesina, Giulio & Ferrari, Chiara & Muscio, Alberto & Pedrazzi, Simone, 2019. "Easy to implement ventilated sunspace for energy retrofit of condominium buildings with balconies," Renewable Energy, Elsevier, vol. 141(C), pages 541-548.
    3. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    4. Calautit, John Kaiser & O’Connor, Dominic & Tien, Paige Wenbin & Wei, Shuangyu & Pantua, Conrad Allan Jay & Hughes, Ben, 2020. "Development of a natural ventilation windcatcher with passive heat recovery wheel for mild-cold climates: CFD and experimental analysis," Renewable Energy, Elsevier, vol. 160(C), pages 465-482.
    5. Pal, Monalisa & Alyafi, Amr Alzouhri & Ploix, Stéphane & Reignier, Patrick & Bandyopadhyay, Sanghamitra, 2019. "Unmasking the causal relationships latent in the interplay between occupant’s actions and indoor ambience: A building energy management outlook," Applied Energy, Elsevier, vol. 238(C), pages 1452-1470.
    6. Wei, Shuangyu & Tien, Paige Wenbin & Calautit, John Kaiser & Wu, Yupeng & Boukhanouf, Rabah, 2020. "Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method," Applied Energy, Elsevier, vol. 277(C).
    7. Calautit, John Kaiser & Tien, Paige Wenbin & Wei, Shuangyu & Calautit, Katrina & Hughes, Ben, 2020. "Numerical and experimental investigation of the indoor air quality and thermal comfort performance of a low energy cooling windcatcher with heat pipes and extended surfaces," Renewable Energy, Elsevier, vol. 145(C), pages 744-756.
    8. Eziaku Onyeizu Rasheed & Hugh Byrd & Blair Money & Jasper Mbachu & Temtiope Egbelakin, 2017. "Why Are Naturally Ventilated Office Spaces Not Popular in New Zealand?," Sustainability, MDPI, vol. 9(6), pages 1-16, May.
    9. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    10. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    11. Rouleau, Jean & Gosselin, Louis, 2020. "Probabilistic window opening model considering occupant behavior diversity: A data-driven case study of Canadian residential buildings," Energy, Elsevier, vol. 195(C).
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    Cited by:

    1. John Kaiser Calautit & Hassam Nasarullah Chaudhry, 2022. "Sustainable Buildings: Heating, Ventilation, and Air-Conditioning," Energies, MDPI, vol. 15(21), pages 1-5, November.
    2. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    3. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alborg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2022. "Recent Advances in Technologies, Methods, and Economic Analysis for Sustainable Development of Energy, Water, and Environment Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
    4. Murtaza Mohammadi & John Calautit, 2021. "Impact of Ventilation Strategy on the Transmission of Outdoor Pollutants into Indoor Environment Using CFD," Sustainability, MDPI, vol. 13(18), pages 1-18, September.

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