CAREER: A Scalable Occupant-Driven Energy Optimization System for Commercial Buildings

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This project seeks to fundamentally change how people live their everyday lives towards a more environmentally responsible and sustainable future and has the potential to play a major role in reducing our reliance on fossil fuels and combating climate change. This research will help enable individuals to reduce their personal energy usage in indoor commercial building settings by providing real-time visibility into the energy cost of each action. This will enable energy-accountability in commercial buildings by providing actionable recommendations with quantifiable savings, as well as insights for both occupants and building managers so that they can act in a timely manner. These small savings can add up: even a small 1% reduction in commercial building energy consumption translates to 1.5 billion dollars of annual savings for the nation. This project will help advance research at the intersection of building-scale systems, Internet-of-Things, wearable systems, and recommender systems. Course modules developed on embedded systems, mobile computing, Internet-of-Things, and deep reinforcement learning will be used to train undergraduate and graduate students.This proposal aims to significantly reduce energy consumption in commercial buildings by examining each occupant’s unique and individualized energy usage, or “energy footprint”, and providing occupants with actionable and measurable energy-saving recommendations. The proposed system comprises of several components, including real-time sensing and actuation, building energy monitoring, indoor localization, large-scale time-series data analytics, and recommender systems. This project is organized into three research thrusts: (1) develop a digital twin model of a commercial building to simulate energy savings from human-driven actions, and research efficient algorithms for computing each occupant’s “energy footprint” in shared environments. (2) advance knowledge in deep reinforcement learning and design a recommender system to discover actions that have the best potential for saving energy while adapting to user preferences. (3) investigate effective feedback mechanisms and incentive schemes to encourage energy saving behaviors, increase recommendation quality, and improve recommendation acceptance rate.Research results, including datasets, embedded hardware designs, software code, simulators, smartphone application code, reports, presentations, and papers will be shared with the research community through the research group website and GitHub. To ensure privacy, any personally identifiable information in datasets will be removed. To encourage deployment and experimentation by others, documented open-source software and hardware will be release and maintain in the project’s GitHub repository at https://github.com/Columbia-ICSL/EnergyFootprinting for at least five years.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.