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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

IoT-based Analysis of Environmental and Motion Data for Comfort and Energy Conservation in Optimizing HVAC Systems

Badmus, A, McGarry, Kenneth, Baglee, David and Eliot, Neil (2024) IoT-based Analysis of Environmental and Motion Data for Comfort and Energy Conservation in Optimizing HVAC Systems. In: The 25th International Conference on Internet Computing & IoT, 22-25 Jul 2024, Las Vegas, USA. (In Press)

Item Type: Conference or Workshop Item (Paper)


Growing energy consumption from campus infrastructure including lecture halls that run heating, ventilation and air conditioning (HVAC) systems motivates data-driven optimization. This research demonstrates an integrated application of Internet of Things (IoT) sensors and cloud-hosted predictive data analytics to enable smart lecture room policies improving efficiency and sustainability. A Raspberry Pi Pico W IoT device was interfaced with BME680 sensor for temperature, humidity and air quality data. The device also incorporated a PIR sensor for occupancy detection and Wi-Fi connectivity to transmit multivariate time series data. The prototype was installed in a university lecture room for real-time data capture. Data was directed to a cloud analytics pipeline including MySQL storage and Node-RED for pre-processing. Time series forecasting was conducted by training autoregressive integrated moving average (ARIMA), Prophet and machine learning models on historical data to predict temperature, occupancy levels, and usage patterns 24 hours into the future. An interactive dashboard visualized both real-time streams and model forecasts using Grafana for analytical insights.

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More Information

Depositing User: Kenneth McGarry


Item ID: 17737
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD
ORCID for David Baglee: ORCID iD
ORCID for Neil Eliot: ORCID iD

Catalogue record

Date Deposited: 03 Jul 2024 10:45
Last Modified: 03 Jul 2024 10:45


Author: Kenneth McGarry ORCID iD
Author: David Baglee ORCID iD
Author: Neil Eliot ORCID iD
Author: A Badmus

University Divisions

Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence

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