暖通空调
能源消耗
旅游
计算机科学
节能
背景(考古学)
朴素贝叶斯分类器
通风(建筑)
能源管理
空调
模拟
能量(信号处理)
工程类
人工智能
统计
地理
机械工程
电气工程
考古
支持向量机
数学
作者
Debin Zhao,Zhengyuan Hu,Yinjian Yang,Qian Chen
出处
期刊:Sustainability
[MDPI AG]
日期:2022-09-22
卷期号:14 (19): 11997-11997
被引量:1
摘要
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.
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