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Energy Conservation for Indoor Attractions Based on NRBO-LightGBM

Author

Listed:
  • Debin Zhao

    (Kunshan Xuanlife Information Technology Co., Ltd., Institute of Big Data, Nanjing 210012, China)

  • Zhengyuan Hu

    (Kunshan Xuanlife Information Technology Co., Ltd., Institute of Big Data, Nanjing 210012, China)

  • Yinjian Yang

    (Kunshan Xuanlife Information Technology Co., Ltd., Institute of Big Data, Nanjing 210012, China)

  • Qian Chen

    (Kunshan Xuanlife Information Technology Co., Ltd., Institute of Big Data, Nanjing 210012, China)

Abstract

In the context of COVID-19, energy conservation is becoming increasingly crucial to the overwhelmed tourism industry, and the heating, ventilation, and air conditioning system (HVAC) is the most energy-consuming factor in the indoor area of scenic spots. As tourist flows are not constant, the intelligent control of an HVAC system is the key to tourist satisfaction and energy consumption management. This paper proposes a noise-reduced and Bayesian-optimized (NRBO) light-gradient-boosting machine (LightGBM) to predict the probability of tourists entering the next scenic spot, hence adopting the feedforward dynamic adaptive adjustment of the ventilation and air conditioning system. The customized model is more robust and effective, and the experimental results in Luoyang City Hall indicate that the proposed system outperforms the baseline LightGBM model and a random-search based method concerning prediction loss by 5.39% and 4.42%, respectively, and saves energy by 23.51%. The study illustrates a promising step in the advancement of tourism energy consumption management and sustainable tourism in the experimental area by improving tourist experiences and conserving energy efficiently, and the software-based system can also be smoothly applied to other indoor scenic spots.

Suggested Citation

  • Debin Zhao & Zhengyuan Hu & Yinjian Yang & Qian Chen, 2022. "Energy Conservation for Indoor Attractions Based on NRBO-LightGBM," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11997-:d:922457
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    References listed on IDEAS

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    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
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