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Smart Buildings: State-Of-The-Art Methods and Data-Driven Applications

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References

  1. M.F.R. Lee, Y.C. Chen, C.Y. Tsai, Deep learning-based human body posture recognition and tracking for unmanned aerial vehicles. Processes 10(11), 2295 (2022)

    Article  Google Scholar 

  2. A. Ayala, B. Fernandes, F. Cruz, D. Macêdo, A. L. Oliveira, Zanchettin, C., Kutralnet: A portable deep learning model for fire recognition, in 2020 International Joint Conference on Neural Networks. (IEEE, 2020), pp. 1–8

    Google Scholar 

  3. H.B. Gunay, W. Shen, C. Yang, Text-mining building maintenance work orders for component fault frequency. Build. Res. Inf. 47(5), 518–533 (2019)

    Article  Google Scholar 

  4. Z. Ding, Z. Li, C. Fan, Building energy savings: Analysis of research trends based on text mining. Autom. Constr. 96, 398–410 (2018)

    Article  Google Scholar 

  5. C. Fan, M. Chen, X. Wang, J. Wang, B. Huang, A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Front. Energy Res. 9, 652801 (2021)

    Article  Google Scholar 

  6. L. Zhu, D. J. Hill, C. Lu, Auto-Starting Semi-Supervised Learning-Based Identification of Synchrophasor Data Anomalies. IEEE Internet Things J. (2022)

    Google Scholar 

  7. F. Xiao, C. Fan, Building information modeling and building automation systems data integration and big data analytics for building energy managementm in Research Companion to Building Information Modeling (2022), pp. 525–549

    Google Scholar 

  8. C. Fan, F. Xiao, Y. Zhao, J. Wang, Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data. Appl. Energy 211, 1123–1135 (2018)

    Article  Google Scholar 

  9. C.W. Chen, C.C. Li, C.Y. Lin, Combine clustering and machine learning for enhancing the efficiency of energy baseline of chiller system. Energies 13(17), 4368 (2020)

    Article  Google Scholar 

  10. C. Fan, Y.T. Lei, Y.J. Sun, M.S. Piscitelli, R. Chiosa, A. Capozzoli, Data-centric or algorithm centric: exploiting the performance of transfer learning for improving building energy predictions in data-scarce context. Energy 240, 122775 (2022)

    Article  Google Scholar 

  11. X. Xu, F. Xiao, S. Wang, Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods. Appl. Therm. Eng. 28(2–3), 226–237 (2008)

    Article  Google Scholar 

  12. X.J. Luo, K.F. Fong, Y.J. Sun, M.K.H. Leung, Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system. Energy Build. 186, 17–36 (2019)

    Article  Google Scholar 

  13. W. Yao, D. Li, L. Gao, Fault detection and diagnosis using tree-based ensemble learning methods and multivariate control charts for centrifugal chillers. J. Build. Eng. 51, 104243 (2022)

    Article  Google Scholar 

  14. S. Taheri, B. Talebjedi, T. Laukkanen, Electricity demand time series forecasting based on empirical mode decomposition and long short-term memory. Energy Eng. 118(6), 1577–1594 (2021)

    Article  Google Scholar 

  15. J. Liu, Q. Zhang, X. Li, G. Li, Z. Liu, Y. Xie, B. Liu, Transfer learning-based strategies for fault diagnosis in building energy systems. Energy Build. 250, 111256 (2021)

    Article  Google Scholar 

  16. C. Fan, W. He, Y. Liu, P. Xue, Y. Zhao, A novel image-based transfer learning framework for cross-domain HVAC fault diagnosis: from multi-source data integration to knowledge sharing strategies. Energy Build. 262, 111995 (2022)

    Article  Google Scholar 

  17. X. Yang, Z. Song, I. King, Z. Xu, A survey on deep semi-supervised learning. IEEE Trans. Knowl. Data Eng. (2022)

    Google Scholar 

  18. B. Li, F. Cheng, X. Zhang, C. Cui, W. Cai, A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data. Appl. Energy 285, 116459 (2021)

    Article  Google Scholar 

  19. C. Fan, Y. Liu, X. Liu, Y. Sun, J. Wang, A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data. Sustain. Cities Soc. 70, 102874 (2021)

    Article  Google Scholar 

  20. K. Ohri, M. Kumar, Review on self-supervised image recognition using deep neural networks. Knowl.-Based Syst. 224, 107090 (2021)

    Article  Google Scholar 

  21. G. Li, J. Wu, C. Deng, M. Wei, X. Xu, Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data. Appl. Acoust. 191, 108663 (2022)

    Article  Google Scholar 

  22. J. Yoon, Y. Zhang, J. Jordon, M. van der Schaar, Vime: Extending the success of self-and semi-supervised learning to tabular domain. Adv. Neural. Inf. Process. Syst. 33, 11033–11043 (2020)

    Google Scholar 

  23. C. Fan, Y.T. Lei, Y.J. Sun, L.K. Mo, Novel transformer-based self-supervised learning methods for improved HVAC fault diagnosis performance with limited labeled data. Energy 278, 127972 (2023)

    Google Scholar 

  24. L.M. Candanedo, V. Feldheim, Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy Build. 112, 28–39 (2016)

    Article  Google Scholar 

  25. Y.R. Yoon, Y.R. Lee, S.H. Kim, J.W. Kim, H.J. Moon, A non-intrusive data-driven model for detailed occupants’ activities classification in residential buildings using environmental and energy usage data. Energy Build. 256, 111699 (2022)

    Article  Google Scholar 

  26. B. Yang, X. Cheng, D. Dai, T. Olofsson, H. Li, A. Meier, Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings. Build. Environ. 162, 106284 (2019)

    Article  Google Scholar 

  27. H. Park, D.Y. Park, Prediction of individual thermal comfort based on ensemble transfer learning method using wearable and environmental sensors. Build. Environ. 207, 108492 (2022)

    Article  Google Scholar 

  28. S. Zemouri, Y. Gkoufas, & J. Murphy, A machine learning approach to indoor occupancy detection using non-intrusive environmental sensor data. in Proceedings of the 3rd international conference on big data and internet of things, (2019), pp. 70–74

    Google Scholar 

  29. O. Ardakanian, A. Bhattacharya, D. Culler, Non-intrusive occupancy monitoring for energy conservation in commercial buildings. Energy Build. 179, 311–323 (2018)

    Article  Google Scholar 

  30. A. Meier, W. Dyer, C. Graham, Using human gestures to control a building’s heating and cooling System. in Proceedings of the 9th International Conference on Energy Efficiency in Domestic Appliances and Lighting (2017), pp. 627–635

    Google Scholar 

  31. A. Ghahramani, G. Castro, B. Becerik-Gerber, X. Yu, Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort. Build. Environ. 109, 1–11 (2016)

    Article  Google Scholar 

  32. P. Jayathissa, M. Quintana, M. Abdelrahman, C. Miller, Humans-as-a-sensor for buildings—intensive longitudinal indoor comfort models. Buildings 10(10), 174 (2020)

    Article  Google Scholar 

  33. G. Song, Z. Ai, G. Zhang, Y. Peng, W. Wang, Y. Yan, Using machine learning algorithms to multidimensional analysis of subjective thermal comfort in a library. Build. Environ. 212, 108790 (2022)

    Article  Google Scholar 

  34. M. Khalil, S. McGough, Z. Pourmirza, M. Pazhoohesh, S. Walker, Transfer learning approach for occupancy prediction in smart buildings. in 2021 12th International Renewable Engineering Conference. (IEEE, 2021), pp. 1–6

    Google Scholar 

  35. B. Brik, M. Esseghir, L. Merghem-Boulahia, H. Snoussi, An IoT-based deep learning approach to analyse indoor thermal comfort of disabled people. Build. Environ. 203, 108056 (2021)

    Article  Google Scholar 

  36. W. He, Y. Ma, C. Fan, X. Wang, B. Huang, Non-intrusive Indoor Occupancy Detection Methods Based on Machine Learning Techniques, in International Symposium on Advancement of Construction Management and Real Estate. (Springer, Singapore, 2022), pp.1186–1201

    Google Scholar 

  37. R. Tang, C. Fan, F. Zeng, W. Feng, Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression. In Building simulation, Vol. 15, No. 3, pp. 317–331 (Tsinghua University Press, 2022)

    Google Scholar 

  38. J. Drgoňa, J. Arroyo, I.C. Figueroa, D. Blum, K. Arendt, D. Kim, L. Helsen, All you need to know about model predictive control for buildings. Annu. Rev. Control. 50, 190–232 (2020)

    Article  MathSciNet  Google Scholar 

  39. A. Domahidi, F. Ullmann, M. Morari, C.N. Jones, Learning decision rules for energy efficient building control. J. Process Control 24(6), 763–772 (2014)

    Article  Google Scholar 

  40. Q. Fu, Z. Han, J. Chen, Y. Lu, H. Wu, Y. Wang, Applications of reinforcement learning for building energy efficiency control: A review. J. Build. Eng. 50, 104165 (2022)

    Article  Google Scholar 

  41. E.T. Maddalena, Y. Lian, C.N. Jones, Data-driven methods for building control—A review and promising future directions. Control. Eng. Pract. 95, 104211 (2020)

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the support of this research by the National Natural Science Foundation of China (No. 52278117), the Philosophical and Social Science Program of Guangdong Province, China (GD22XGL20) and the Shenzhen Science and Technology Program (No. 20220531101800001 and 20220810160221001).

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Authors and Affiliations

  1. Key Laboratory for Resilient Infrastructures of Coastal Cities, Ministry of Education, Shenzhen University, Shenzhen, China

    Cheng Fan & Huilong Wang

  2. Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University, Shenzhen, China

    Cheng Fan & Huilong Wang

  3. Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China

    Fu Xiao

Authors
  1. Cheng Fan
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  2. Fu Xiao
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  3. Huilong Wang
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Corresponding author

Correspondence to Fu Xiao .

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Editors and Affiliations

  1. Department of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Xinyan Huang

  2. Fire Research Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Wai Cheong Tam

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Fan, C., Xiao, F., Wang, H. (2024). Smart Buildings: State-Of-The-Art Methods and Data-Driven Applications. In: Huang, X., Tam, W.C. (eds) Intelligent Building Fire Safety and Smart Firefighting. Digital Innovations in Architecture, Engineering and Construction. Springer, Cham. https://doi.org/10.1007/978-3-031-48161-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-48161-1_3

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  • Online ISBN: 978-3-031-48161-1

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