An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction

Energies

Roberto Girau¹  Alessandro Floris²  Simone Porcu²  Luigi Atzori² 

[1]Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy

[2]Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy

关键词: Internet of Things;    smart building;    occupants prediction;    environmental parameters;    data analytics;   

DOI  :  10.3390/en14102959

来源: DOAJ

【 摘 要 】

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

【 授权许可】

Unknown