  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. 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 non-cooperative 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. A benchmark
utility learning framework that employs robust estimations for classical
discrete choice models provided for the derived 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
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 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.
