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UK Workshop on Computational Intelligence

UKCI 2019: Advances in Computational Intelligence Systems pp 395–408Cite as

iBuilding: Artificial Intelligence in Intelligent Buildings

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1043))

Abstract

This paper presents iBuilding: Artificial Intelligence embedded into Intelligent Buildings that adapts to the external environment and the different building users. Buildings are becoming more intelligent in the way they monitor the usage of its assets, functionality and space; the more efficient a building can be monitored or predicted, the more return of investment can deliver as unused space or energy can be redeveloped or commercialized, reducing energy consumption while increasing functionality. This paper proposes Artificial Intelligence embedded into a Building based on a simple Deep Learning structure and Reinforcement Learning algorithm. Sensorial neurons are dispersed through the Intelligent Building to gather and filter environment information whereas Management Sensors based on Reinforcement Learning algorithm make predictions about values and trends in order for building managers or developers to make commercial or operational informed decisions. The proposed iBuilding is validated with a research dataset. The results show that Artificial Intelligence embedded into the Intelligent Building enables real time monitoring and successful predictions about its variables; although there is further research to improve the algorithm’s performance as the results are not optimum.

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References

  1. Ghaffarianhoseini, A., Berardi, U., AlWaer, H., Chang, S., Halawa, E., Ghaffarianhoseini, A., Clements, D.: What is an intelligent building? Analysis of recent interpretations from an international perspective. Arch. Sci. Rev. 59(5), 338–357 (2016)

    Article  Google Scholar 

  2. Clements, D.: What do we mean by intelligent buildings? Autom. Construction. 6(5–6), 395–400 (1997)

    Google Scholar 

  3. Omar, O.: Intelligent building, definitions, factors and evaluation criteria of selection. Alex. Eng. J. 57, 2903–2910 (2018)

    Article  Google Scholar 

  4. Leclercq, A., Isaac, H.: The new office: how coworking changes the work concept. J. Bus. Strat. 37(6), 3–9 (2016)

    Article  Google Scholar 

  5. Hui, J.K., Zhang, Y.: Sharing space: urban sharing, sharing a living space, and shared social spaces. Space Cult., 1–13 (2018)

    Google Scholar 

  6. Yacoub, G.: How do collaborative practices emerge in coworking spaces? Evidence from fintech start-ups. Acad. Manag. Proc. 1, 1–10 (2018)

    Google Scholar 

  7. Honga, T., Taylor-Langea, S., D’Ocab, S., Yanc, D., Corgnati, S.: Advances in research and applications of energy-related occupant behaviour in buildings. Energy Build. 116, 694–702 (2016)

    Article  Google Scholar 

  8. Masoso, O.T., Grobler, L.J.: The dark side of occupants’ behaviour on building energy use. Energy Build. 42, 173–177 (2010)

    Article  Google Scholar 

  9. Hong, T., Yan, D., D’Oca, S., Chen, C.: Ten questions concerning occupant behaviour in buildings: the big picture. Build. Environ. 114, 518–530 (2017)

    Article  Google Scholar 

  10. Nguyen, T., Aiello, M.: Energy intelligent buildings based on user activity: a survey. Energy Build. 56, 244–257 (2013)

    Article  Google Scholar 

  11. Shaikh, P., Nor, N., Nallagownden, P., Elamvazuthi, I., Ibrahim, T.: A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renew. Sustain. Energy Rev. 34, 409–429 (2014)

    Article  Google Scholar 

  12. Oldewurtel, F., Sturzenegger, D., Morari, M.: Importance of occupancy information for building climate control. Appl. Energy 101, 521–532 (2013)

    Article  Google Scholar 

  13. Labeodan, T., Zeiler, W., Boxem, G., Zhao, Y.: Occupancy measurement in commercial office buildings fordemand-driven control applications—A survey and detection system evaluation. Energy Build. 93, 303–314 (2015)

    Article  Google Scholar 

  14. Ko, J.H., Kong, D.S., Huh, J.H.: Baseline building energy modeling of cluster inverse model by using daily energy consumption in office buildings. Energy Build. 140, 317–323 (2017)

    Article  Google Scholar 

  15. Cetina, K.S., Tabares-Velascob, P.C., Novoselac, A.: Appliance daily energy use in new residential buildings: use profiles and variation in time-of-use. Energy Build. 84, 716–726 (2014)

    Article  Google Scholar 

  16. Tüfekci, P.: Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Electr. Power Energy Syst. 60, 126–140 (2014)

    Article  Google Scholar 

  17. Wang, Z., Srinivasan, R.S.: A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renew. Sustain. Energy Rev. 75, 796–808 (2017)

    Article  Google Scholar 

  18. Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build. 49, 560–567 (2012)

    Article  Google Scholar 

  19. Arghira, N., Hawarah, L., Ploix, S., Jacomino, M.: Prediction of appliances energy use in smart homes. Energy 48, 128–134 (2012)

    Article  Google Scholar 

  20. Candanedo, L.M., Feldheim, V., Deramaix, D.: Data driven prediction models of energy use of appliances in a low-energy house. Energy Build. 140, 81–97 (2017)

    Article  Google Scholar 

  21. Bi, H., Gelenbe, E.: A cooperative emergency navigation framework using mobile cloud computing. In: International Symposium Computer and Information Sciences, pp. 41–48 (2014)

    Google Scholar 

  22. Gelenbe, E., Bi, H.: Emergency navigation without an infrastructure. Sensors 14(8), 15142–15162 (2014)

    Article  Google Scholar 

  23. Gelenbe, E.: Cognitive Packet Network. Patent US 6804201 B1 (2004)

    Google Scholar 

  24. Gelenbe, E., Xu, Z., Seref, E.: Cognitive packet networks. In: International Conference on Tools with Artificial Intelligence, pp. 47–54 (1999)

    Google Scholar 

  25. Gelenbe, E., Lent, R., Xu, Z.: Networks with cognitive packets. In: IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 3–10 (2000)

    Google Scholar 

  26. Gelenbe, E., Lent, R., Xu, Z.: Measurement and performance of a cognitive packet network. Comput. Netw. 37(6), 691–701 (2001)

    Article  Google Scholar 

  27. Gelenbe, E., Lent, R., Montuori, A., Xu, Z.: Cognitive packet networks: QoS and performance. In: IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 3–9 (2002)

    Google Scholar 

  28. Bi, H., Desmet, A., Gelenbe, E.: Routing emergency evacuees with cognitive packet networks. In: International Symposium on Computer and Information Sciences, pp. 295–303. (2013)

    Chapter  Google Scholar 

  29. Bi, H., Gelenbe, E.: Routing diverse evacuees with cognitive packets. In: IEEE Computer Society PerCom Workshops, pp. 291–296 (2014)

    Google Scholar 

  30. Gelenbe, E.: Random neural networks with negative and positive signals and product form solution. Neural Comput. 1, 502–510 (1989)

    Article  Google Scholar 

  31. Gelenbe, E.: Learning in the recurrent random neural network. Neural Comput. 5, 154–164 (1993)

    Article  Google Scholar 

  32. Gelenbe, E.: G-networks with triggered customer movement. J. Appl. Probab. 30, 742–748 (1993)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Systems and Networks Group, Electrical and Electronic Engineering, Alumni Imperial College London, London, UK

    Will Serrano

Authors
  1. Will Serrano
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Corresponding author

Correspondence to Will Serrano .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  3. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

    Chenguang Yang

  4. School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK

    Alexander Gegov

  5. School of Computing, University of Portsmouth, Portsmouth, UK

    Dalin Zhou

Appendix: iBuilding Neural Schematic

Appendix: iBuilding Neural Schematic

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Serrano, W. (2020). iBuilding: Artificial Intelligence in Intelligent Buildings. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_33

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