ABSTRACT

Internet of Things (IoT) empowered Heating, Ventilation, and Air Conditioning (HVAC) buildings are considered to monitor and control the regulation of thermostats, sensors, actuators, and control devices smartly. In this article, we propose a novel model named PersonalisedComfort to predict the thermal sensation votes of individuals living in a building. We use publicly available standard dataset ASHRAE RP-884 for experimentation and analysis. We apply conventional machine learning algorithms and deep learning algorithms to predict the thermal sensation vote. PersonalisedComfort achieves an accuracy of 85% to predict thermal sensation votes which 8% higher than state-of-the-art studies.

摘要

物联网 (IoT) 支持供暖、通风和空调 (HVAC) 建筑物被认为可以智能地监控和控制恒温器、传感器、执行器和控制设备的调节。在本文中，我们提出了一个名为PersonalizedComfort的新模型来预测居住在建筑物中的个人的热感觉投票。我们使用公开可用的标准数据集ASHRAE RP-884进行实验和分析。我们应用传统的机器学习算法和深度学习算法来预测热感觉投票。PersonalizedComfort预测热感觉投票的准确率达到 85%，比最先进的研究高 8%。