A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure
| Autor: | Huihan Liu, Andrew R. Barkan, Tanya Veeravalli, Costas J. Spanos, Ioannis C. Konstantakopoulos, Shiying He |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 020209 energy Mechanical Engineering Deep learning Context (language use) 02 engineering and technology Building and Construction Management Monitoring Policy and Law General Energy Resource (project management) 020401 chemical engineering Human–computer interaction 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Operational efficiency Artificial intelligence 0204 chemical engineering business Game theory Efficient energy use Building automation |
| Zdroj: | Applied Energy. 237:810-821 |
| ISSN: | 0306-2619 |
| DOI: | 10.1016/j.apenergy.2018.12.065 |
| Popis: | 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 ) |
| Databáze: | OpenAIRE |
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