Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

Abstract

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3–4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%.

Indoor Environment Dataset to Estimate Room Occupancy

“…It is important to realize that some steps of the methodology used to generate the different resolution datasets differ from those used in the previous publication [17]. Therefore, the resulting datasets, although similar, are not exactly the same.…”

“…This dataset was generated as part of efforts for a previous research publication [17]. The data collected belong to two different enclosed spaces.…”

“…Therefore, this data descriptor contributes to the goal of having a reference dataset by presenting a dataset on indoor environmental variables and information related to the occupation of two different rooms. These data were collected for the occupancy estimation research presented in Vela et al [17]; they are now described in greater detail and are freely available to researchers and the general public through the Mendeley Data open data repository. Furthermore, due to the contrasting characteristics of the enclosed spaces used in the collection (e.g., design use of the spaces, type of ventilation, and size), this dataset is a valuable contribution to the field of occupancy information.…”

Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy

“…Many studies that have been published focused on the estimation of occupancy in enclosed buildings using machine learning algorithms. Amongst the recent research published in the past five years, environmental variables have been used extensively for the development of machine learning models for occupancy detection [4][5][6][7]. This is partly because these sensors used for the data collection are readily available in the market at economical prices.…”

“…In a study conducted by Kumar et al, machine learning techniques were explored to develop an occupancy detection model based on environmental factors such as date and time, temperature, humidity, light, the level of carbon dioxide (CO 2 ) and humidity ratio [4]. Similarly, Vela et al adjudged the KNN as the best performing machine learning classification model to estimate occupancy based on environmental variables (that is humidity, temperature, and pressure) [5]. Data fusion of environmental sensors were confirmed to validate individual sensors for improved performance [7].…”