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A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure

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  • Konstantakopoulos, Ioannis C.
  • Barkan, Andrew R.
  • He, Shiying
  • Veeravalli, Tanya
  • Liu, Huihan
  • Spanos, Costas

Abstract

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to consider personal energy usage and to have positive effects on their environment. Human interaction in the context of cyber-physical systems is a core component and consideration in the implementation of any smart building technology. Research has shown that the adoption of human-centric building services and amenities leads to improvements in the operational efficiency of these cyber-physical systems directed toward controlling building energy usage. We introduce a strategy that incorporates humans-in-the-loop modeling by creating an interface to allow building managers to interact with occupants and potentially incentivize energy efficient behavior. Game theoretic analysis typically relies on the assumption that the utility function of each individual agent is known a priori. Instead, we propose a novel benchmark utility learning framework that employs robust estimations of occupant actions toward energy efficiency. To improve forecasting performance, we extend the benchmark utility learning scheme by leveraging Deep Learning end-to-end training with deep bi-directional Recurrent Neural Networks. We apply the proposed methods to high-dimensional data from a social game experiment designed to encourage energy efficient behavior among smart building occupants. Using data gathered from occupant actions for resources such as room lighting, we forecast patterns of resource usage to demonstrate the performance of the proposed methods on ground truth data. The results of our study show that we can achieve a highly accurate representation of the ground truth for occupant resource usage. For demonstrations of our infrastructure and for downloading de-identified, high-dimensional data sets, please visit our website (smartNTU demo web portal: https://smartntu.eecs.berkeley.edu)

Suggested Citation

  • Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.
  • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:810-821
    DOI: 10.1016/j.apenergy.2018.12.065
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    1. Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    2. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    3. Guo, Qingbin & Wang, Yong & Dong, Xiaobin, 2022. "Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China," Applied Energy, Elsevier, vol. 313(C).
    4. Hernández, José L. & de Miguel, Ignacio & Vélez, Fredy & Vasallo, Ali, 2024. "Challenges and opportunities in European smart buildings energy management: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    5. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
    6. Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
    7. Tien, Paige Wenbin & Wei, Shuangyu & Calautit, John Kaiser & Darkwa, Jo & Wood, Christopher, 2022. "Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand," Applied Energy, Elsevier, vol. 308(C).
    8. Su, Bing & Wang, Shengwei, 2020. "An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks," Applied Energy, Elsevier, vol. 274(C).
    9. Jafari, Hamed & Safarzadeh, Soroush & Azad-Farsani, Ehsan, 2022. "Effects of governmental policies on energy-efficiency improvement of hydrogen fuel cell cars: A game-theoretic approach," Energy, Elsevier, vol. 254(PC).
    10. Ali Ghofrani & Esmat Zaidan & Mohsen Jafari, 2021. "Reshaping energy policy based on social and human dimensions: an analysis of human-building interactions among societies in transition in GCC countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-26, December.
    11. Elnour, Mariam & Fadli, Fodil & Himeur, Yassine & Petri, Ioan & Rezgui, Yacine & Meskin, Nader & Ahmad, Ahmad M., 2022. "Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    12. Rajat Gupta & Sahar Zahiri & Johanna Morey, 2023. "Enhancing User Engagement in Local Energy Initiatives Using Smart Local Energy Engagement Tools: A Meta Study," Energies, MDPI, vol. 16(7), pages 1-25, March.
    13. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    14. Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.

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