Abstract
Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones. Furthermore, when detecting an indoor thermal discomfort, we provide a new genetic-based algorithm to find the most suitable values of indoor parameters, enabling the improvement of the indoor occupants’ thermal comfort. Numerical results show the efficiency of our prediction scheme, reaching an accuracy of 96%, as well as our genetic-based scheme in optimizing the indoor thermal parameters by 85%.
Highlights
Nowdays, people spend 87% of their time indoors, in addition to 6% in an enclosed vehicle, according to a U.S Environmental Protection Agency (EPA) survey [1]
We propose an Internet of Things (IoT)-based architecture supporting the prediction and control of indoor thermal comfort parameters, remotely
Our architecture enables sensed data to be sent to remote Cloud servers for further storage and analysis in order to measure and predict the indoor thermal comfort parameters
Summary
People spend 87% of their time indoors, in addition to 6% in an enclosed vehicle, according to a U.S Environmental Protection Agency (EPA) survey [1]. Indoor thermal comfort monitoring has attracted a great deal of research attention in recent years, since it improves the occupants’ comfort and the efficiency of energy consumption in residential buildings [3,4,5]. This immense attraction is mainly due to the emergence of Internet of Things (IoT) technology that connects objects to the Internet through ubiquitous sensors [6]. The IoT helps to deal with the indoor thermal comfort monitoring in real time [7]
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