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iBuilding: Artificial Intelligence in Intelligent Buildings

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Advances in Computational Intelligence Systems (UKCI 2019)

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

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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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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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