Work

Design, comparison and application of artificial intelligence predictive models based on experimental data for estimating carbon dioxide concentration inside a building

Year: 2024

Type: article

Abstract: This paper outlines the development of an affordable microclimate station built with Wi-Fi-enabled microcontrollers similar to Arduino, designed to monitor environmental factors such as temperature, humidity, human presence, atmospheric pressure, and carbon dioxide levels. Installed in a primary school classroom in Bialystok, Poland, the station gathers data on a minute-by-minute basis. This data is then utilized to create and assess predictive models for estimating indoor CO2 levels, even in the absence of a direct CO2 sensor. Among the various models tested, the random forest method, which relied solely on temperature, humidity, and human presence measurements, produced the most accurate results, achieving an R-squared value of 0.89. The use of temperature, humidity, and presence sensors, which are more affordable than CO2 sensors, highlights the cost-effectiveness of predictive modeling in environmental monitoring.

Source: Applied Thermal Engineering

DOI: 10.1016/j.applthermaleng.2024.125122

Authors: Vincenzo Ballerini, Paolo Valdiserri, Dorota Anna Krawczyk, Beata Sadowska, Bernadetta Lubowicka, Eugenia Rossi di Schio

Institutions: University of Bologna, Bialystok University of Technology

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,790