Abstract
The building sector holds a significant impact over global energy usage and carbon emissions, making effective building energy management vital for ensuring worldwide sustainability and meeting climate goals. In line with this objective, this study aims to develop and demonstrate an innovative smart data-driven framework for building energy management. The framework includes semantic multi-source data integration schema, AI-empowered data-driven optimization and predictive maintenance strategies, and digital twin for informative and interactive human-equipment-information building management platform. A case study was conducted in a typical chiller plant on a campus located in Hong Kong, China. The results show that the deployment of the proposed smart data-driven framework achieves chiller sequencing control in a more robust and energy-efficient manner. Specifically, the proposed control strategy achieves energy savings of 5.9% to 12.2% compared to the conventional strategy. This research represents an important step forward in the development of smarter and more sustainable building management practices, which will become increasingly critical as we strive to reduce our environmental impact and combat climate change.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
IEA (2022), Buildings, IEA, Paris https://www.iea.org/reports/buildings, License: CC BY 4.0
East E W. Construction operations building information exchange (COBie)[R]. Engineer Research and Development Center Champaign Il Construction Engineering Research Lab (2007)
Dave, B., Buda, A., Nurminen, A., et al.: A framework for integrating BIM and IoT through open standards. Autom. Constr.. Constr. 95, 35–45 (2018)
Compton, M., Barnaghi, P., Bermudez, L., et al.: The SSN ontology of the W3C semantic sensor network incubator group. J. Web Semant. 17, 25–32 (2012)
Terkaj, W., Schneider, G.F., Pauwels, P.: Reusing domain ontologies in linked building data: the case of building automation and control. In: 8th International work-shop on formal ontologies meet industry. 2017, 2050
Rasmussen, M.H., Lefrançois, M., Schneider, G.F., et al.: BOT: The building topology ontology of the W3C linked building data group. Semantic Web 12(1), 143–161 (2021)
Terkaj, W., Šojić, A.: Ontology-based representation of IFC EXPRESS rules: an enhancement of the ifcOWL ontology. Autom. Constr.. Constr. 57, 188–201 (2015)
Balaji, B., Bhattacharya, A., Fierro, G., et al.: Brick: Metadata schema for portable smart building applications. Appl. Energy 226, 1273–1292 (2018)
Jiang, F., Ma, L., Broyd, T., Chen, K.: Digital twin and its implementations in the civil engineering sector. Autom. Constr. 130, 103838 (2021)
Chen, W., Chen, K., Cheng, J.C., Wang, Q., Gan, V.J.: BIM-based framework for automatic scheduling of facility maintenance work orders. Autom. Constr. 91, 15–30 (2018)
Chen, W., Chen, K., Cheng, J.C.: From building information modeling to digital twin: the core for sustainable smart campus at HKUST. In: Research Companion to Building Information Modeling, pp. 671–696. Edward Elgar Publishing (2022)
Wang, S., Burnett, J.: Online adaptive control for optimizing variable-speed pumps of indirect water-cooled chilling systems. Appl. Therm. Eng. 21(11), 1083–1103 (2001)
Chen, Z., Xiao, F., Guo, F., Yan, J.: Interpretable machine learning for building energy management: A state-of-the-art review. Advances in Applied Energy, 100123 (2023)
Zhang, L., et al.: A review of machine learning in building load prediction. Appl. Energy 285, 116452 (2021)
Mirnaghi, M.S., Haghighat, F.: Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: a comprehensive review. Energy Build. 229, 110492 (2020)
Ding, Y., Liu, X.: A comparative analysis of data-driven methods in building energy benchmarking. Energy Build. 209, 109711 (2020)
Jin, Y., Yan, D., Chong, A., Dong, B., An, J.: Building occupancy forecasting: a systematical and critical review. Energy Build. 251, 111345 (2021)
Xiao, F., Fan, C.: Building information modeling and building automation systems data integration and big data analytics for building energy management. Research Companion to Building Information Modeling, pp. 525–549 (2022)
Javed, A., Larijani, H., Ahmadinia, A., Emmanuel, R., Mannion, M., Gibson, D.: Design and implementation of a cloud enabled random neural network-based decentralized smart controller with intelligent sensor nodes for HVAC. IEEE Internet Things J. 4(2), 393–403 (2017)
Abdelrahman, M.M., Chong, A., Miller, C.: Personal thermal comfort models using digital twins: preference prediction with BIM-extracted spatial–temporal proximity data from Build2Vec. Build. Environ. 207, 108532 (2022)
Pan, Z., Yu, Y., Xiao, F., Zhang, J.: Recovering building information model from 2D drawings for mechanical, electrical and plumbing systems of ageing buildings. Autom. Constr. 152, 104914 (2023)
Liao, Y., Huang, G.: A hybrid predictive sequencing control for multi-chiller plant with considerations of indoor environment control, energy conservation and economical operation cost. Sustain. Cities Soc. 49, 101616 (2019)
Sun, S., Wang, S., Shan, K.: Flow measurement uncertainty quantification for building central cooling systems with multiple water-cooled chillers using a Bayesian approach. Appl. Therm. Eng. 202, 117857 (2022)
Acknowledgement
The authors gratefully acknowledge the support of this research by the Innovation and Technology Fund (ITP/002/22LP) of the Hong Kong SAR, China, the Hong Kong Polytechnic University Carbon Neutrality Funding Scheme and the E&M AI Lab of Electrical and Mechanical Services Department (EMSD) of Hong Kong SAR, China.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, J. et al. (2024). Smart Data-Driven Building Management Framework and Demonstration. In: Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14467. Springer, Cham. https://doi.org/10.1007/978-3-031-48649-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-48649-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48648-7
Online ISBN: 978-3-031-48649-4
eBook Packages: Computer ScienceComputer Science (R0)