Abstract-The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. Adoption of human-centric building services and amenities also leads to improvements in the operational efficiency of cyber-physical systems that are used to control building energy usage. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestation as control inputs to a building. Furthermore, the occupants of a building typically lack the independent motivation necessary to contribute to and play a key role in the control of smart building infrastructure. Moreover, true human actions and their integration with sensing/actuation platforms remains unknown to the decision maker tasked with improving operational efficiency. By modeling user interaction as a sequential discrete game between noncooperative players, we introduce a gamification approach for supporting user engagement and integration in a human-centric cyber-physical system. We propose the design and implementation of a large-scale network game with the goal of improving the energy efficiency of a building through the utilization of cutting-edge Internet of Things (IoT) sensors and cyber-physical systems sensing/actuation platforms. By observing human decision makers and their decision strategies in their operation of building systems, we can apply inverse learning techniques in order to estimate their utility functions. Game theoretic analysis often relies on the assumption that the utility function of each agent is known a priori; however, this assumption usually does not hold in many real-world applications. We propose a benchmark utility learning framework that employs robust estimations for classical discrete choice models provided with high dimensional imbalanced data. To improve forecasting performance, we extend the benchmark utility learning scheme by leveraging Deep Learning end-to-end training with Deep bidirectional 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 in Nanyang Technological University (NTU) residential housing. Using occupant-retrieved actions for resources such as lighting and A/C, we simulate the game defined by the estimated utility functions to demonstrate the performance of the proposed methods on ground truth data. For demonstrations of our infrastructure and for downloading deidentified high dimensional data sets, please visit our web site 1 .
Leveraging past users' observations, we can learn the utility functions of individual occupants by the way of several noble algorithms REF .
A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University
ArXiv
ArXiv
Mathematics Computer Science