Optimizing indoor environmental prediction in smart buildings: A comparative analysis of deep learning models

Minassian, R; Mihăiţă, AS; Shirazi, A

Optimizing indoor environmental prediction in smart buildings: A comparative analysis of deep learning models

Minassian, R Mihăiţă, AS Shirazi, A

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Publisher:: ELSEVIER SCIENCE SA
Publication Type:: Journal Article
Citation:: Energy and Buildings, 2025, 327
Issue Date:: 2025-01-15

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Field	Value	Language
dc.contributor.author	Minassian, R
dc.contributor.author	Mihăiţă, AS
dc.contributor.author	Shirazi, A https://orcid.org/0000-0002-6629-2902
dc.date.accessioned	2025-07-16T05:18:46Z
dc.date.available	2025-07-16T05:18:46Z
dc.date.issued	2025-01-15
dc.identifier.citation	Energy and Buildings, 2025, 327
dc.identifier.issn	0378-7788
dc.identifier.issn	1872-6178
dc.identifier.uri	http://hdl.handle.net/10453/188437
dc.description.abstract	This paper presents a comprehensive investigation into the application of deep learning models for predicting indoor environmental quality in smart buildings. Using data collected from a network of microclimate sensors deployed across a university campus in Sydney, we evaluated the performance of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. Our study encompassed various aspects of model development, including data preparation, architecture design, hyperparameter optimization, and model interpretability. Contrary to common assumptions in time series forecasting, our results demonstrate that CNN models consistently outperformed LSTM and hybrid models in predicting indoor temperature. We found that multivariate input configurations enhanced prediction accuracy across all model types, highlighting the importance of capturing complex interactions between environmental parameters. Through SHapley Additive exPlanations (SHAP) analysis, we identified temperature, humidity, and Heating, Ventilation, and Air Conditioning (HVAC) status as the most influential features for predictions. Our experiments also revealed optimal configurations for historical input length and prediction horizon, providing practical guidelines for model implementation. This research contributes valuable insights for the development of more efficient and accurate smart building management systems, potentially leading to improved energy efficiency and occupant comfort in built environments.
dc.language	English
dc.publisher	ELSEVIER SCIENCE SA
dc.relation.ispartof	Energy and Buildings
dc.relation.isbasedon	10.1016/j.enbuild.2024.115086
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	09 Engineering, 12 Built Environment and Design
dc.subject.classification	Building & Construction
dc.subject.classification	33 Built environment and design
dc.subject.classification	40 Engineering
dc.title	Optimizing indoor environmental prediction in smart buildings: A comparative analysis of deep learning models
dc.type	Journal Article
utslib.citation.volume	327
utslib.for	09 Engineering
utslib.for	12 Built Environment and Design
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Visualisation Institute (VI)
pubs.organisational-group	University of Technology Sydney/New Faculty
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-16T05:18:44Z
pubs.publication-status	Published
pubs.volume	327

Abstract:

This paper presents a comprehensive investigation into the application of deep learning models for predicting indoor environmental quality in smart buildings. Using data collected from a network of microclimate sensors deployed across a university campus in Sydney, we evaluated the performance of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. Our study encompassed various aspects of model development, including data preparation, architecture design, hyperparameter optimization, and model interpretability. Contrary to common assumptions in time series forecasting, our results demonstrate that CNN models consistently outperformed LSTM and hybrid models in predicting indoor temperature. We found that multivariate input configurations enhanced prediction accuracy across all model types, highlighting the importance of capturing complex interactions between environmental parameters. Through SHapley Additive exPlanations (SHAP) analysis, we identified temperature, humidity, and Heating, Ventilation, and Air Conditioning (HVAC) status as the most influential features for predictions. Our experiments also revealed optimal configurations for historical input length and prediction horizon, providing practical guidelines for model implementation. This research contributes valuable insights for the development of more efficient and accurate smart building management systems, potentially leading to improved energy efficiency and occupant comfort in built environments.

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http://hdl.handle.net/10453/188437