室内环境参数的预测建模,用于评估幼儿园环境中的舒适条件
Radostin Mitkov1, Dessislava Petrova-Antonova1,2, Petar O Hristov1,3
1GATE Institute, Sofia University "St. Kliment Ohridski", 1113 Sofia, Bulgaria.
Toxics
|August 25, 2023
概括
这项研究使用人工智能模型预测室内空气质量,以确保幼儿园的舒适和健康. 长期短期记忆 (LSTM) 模型准确地预测二氧化碳 (CO2),温度和湿度,以便更好地做出决策.
科学领域:
- 环境科学 环境科学
- 建筑物理 建筑物理
- 人工智能的人工智能
背景情况:
- 室内环境对人类健康,舒适度和生产力产生重大影响.
- 了解和控制室内空气质量至关重要,特别是在教育设施等敏感环境中.
- 之前的研究强调了对于室内环境参数的准确预测模型的需求.
研究的目的:
- 开发和比较室内空气质量参数的预测模型.
- 为了预测幼儿园的二氧化碳 (CO2),温度和相对湿度水平.
- 根据预测的室内环境参数来评估全球舒适条件.
主要方法:
- 利用来自保加利亚索非亚幼儿园的测量数据.
- 开发和应用自回归集成移动平均 (ARIMA) 和长期短期记忆 (LSTM) 循环神经网络 (RNN) 模型.
- 使用LSTM预测二氧化碳,温度和相对湿度,并估计全球舒适度.
主要成果:
- 与ARIMA相比,LSTM模型在二氧化碳预测方面表现优越.
- 实现了高预测准确度,温度,湿度和CO2的R2值从0.938到0.981不等.
- 全球舒适条件的预测准确率为91/100.
结论:
- LSTM循环神经网络模型对室内环境参数的近实时预测非常有效.
- 准确预测二氧化碳,温度和湿度可以支持及时干预,以保持最佳的室内条件.
- 该研究为提高教育环境中的室内环境质量和居住者福祉提供了一个框架.
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