Estimation of Unmeasured Room Temperature, Relative Humidity, and CO2 Concentrations for a Smart Building Using Machine Learning and Exploratory Data Analysis

Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variable...

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Vydáno v:Energies Ročník 15; číslo 12; s. 4213
Hlavní autoři: Kaligambe, Abraham, Fujita, Goro, Keisuke, Tagami
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 08.06.2022
Témata:
Accuracy
Algorithms
Artificial intelligence
Case studies
CO2 concentrations
Communication
Communications networks
Data analysis
Energy consumption
estimation
Forecasting
Green buildings
Human error
Humidity
HVAC
Indoor air quality
Internet of Things
Machine learning
Neural networks
Performance evaluation
relative humidity
room temperature
room temperature; relative humidity; CO2 concentrations; estimation; HVAC; machine learning; XGBoost algorithm; smart buildings; sensors; exploratory data analysis
Sensors
Simulation
Software
T
Technology
Variables
Japan
ISSN:1996-1073, 1996-1073
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Shrnutí:Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variables essential for the heating, ventilation, and air conditioning (HVAC) system control strategies. For accuracy and smooth operation, current HVAC system control strategies require multiple sensors to capture the indoor environment variables. However, using too many sensors creates an extensive network that is costly and complex to maintain. Our proposed research solves the mentioned problem by implementing a machine-learning algorithm to estimate unmeasured variables utilizing a limited number of sensors. Using a six-month data set collected from a three-story smart building in Japan, several extreme gradient boosting (XGBoost) models were designed and trained to estimate unmeasured room temperature, relative humidity, and CO2 concentrations. Our models accurately estimated temperature, humidity, and CO2 concentration under various case studies with an average root mean squared error (RMSE) of 0.3 degrees, 2.6%, and 26.25 ppm, respectively. Obtained results show an accurate estimation of indoor environment measurements that is applicable for optimal HVAC system control in smart buildings with a reduced number of required sensors.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en15124213