DigiGuide: A DT-Based Occupant Guiding System for Optimizing Comfort and Energy Consumption

Balancing occupant comfort while minimizing energy consumption is not trivial. Traditional methods rely on environmental control guided by occupant feedback but often fall short in addressing individual preferences effectively. This paper presents DigiGuide, an innovative system that leverages Digital Twin (DT) methodologies combined with multi-objective optimization algorithms to guide occupants to spaces that best meet their multi-variant comfort needs. DigiGuide forecasts future indoor environmental conditions and occupant states in real-time by relying on the DT of the physical environment. It then leverages a genetic algorithm to simultaneously optimize occupant movement guidance to balance comfort needs with energy efficiency. DigiGuide is validated using two realistic large-scale scenarios: a co-working open space and an airport in Paris, France. Results demonstrate that DigiGuide achieves an average of 18.2 % lower discomfort with 8.6 % lower energy consumption compared to baseline approaches.

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Publication Details

Published
2025-06-16
DOI
https://doi.org/10.1109/smartcomp65954.2025.00065
Citations
1
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
0.73
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article

DigiGuide: A DT-Based Occupant Guiding System for Optimizing Comfort and Energy Consumption

Jun Ma, Georgios Bouloukakis, Roberto Yus
1 citations
IoT-based Smart Home Systems
0.73
article

DigiGuide: A DT-Based Occupant Guiding System for Optimizing Comfort and Energy Consumption

Jun Ma, Georgios Bouloukakis, Roberto Yus
article en
1 citations

Abstract

Balancing occupant comfort while minimizing energy consumption is not trivial. Traditional methods rely on environmental control guided by occupant feedback but often fall short in addressing individual preferences effectively. This paper presents DigiGuide, an innovative system that leverages Digital Twin (DT) methodologies combined with multi-objective optimization algorithms to guide occupants to spaces that best meet their multi-variant comfort needs. DigiGuide forecasts future indoor environmental conditions and occupant states in real-time by relying on the DT of the physical environment. It then leverages a genetic algorithm to simultaneously optimize occupant movement guidance to balance comfort needs with energy efficiency. DigiGuide is validated using two realistic large-scale scenarios: a co-working open space and an airport in Paris, France. Results demonstrate that DigiGuide achieves an average of 18.2 % lower discomfort with 8.6 % lower energy consumption compared to baseline approaches.

University of Maryland, Baltimore County (US)
Affordable and clean energy
Openalex Percentile: Top 26%
IoT-based Smart Home Systems
0.73
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