Optimization Framework For Improved Comfort & Efficiency
The term internet of things has evolved by many folds due to the convergence of multiple technologies like machine learning, embedded systems, wireless-sensors, real-time analytics, etc. A growing number of IoT devices are being developed for consumer use, including connected vehicles, home automation, wearable technology, connected health, and remote monitoring devices. We want to apply some of these technologies and techniques to make commercial spaces smarter.
Buildings are responsible for a significant portion of energy consumption in the US, accounting for more than 40% of US primary energy consumption. Heating, ventilation, and air-conditioning (HVAC) accounts for nearly 50% of that use. Conditioning buildings is important since people spend 87% of their time in the place they live (residential) and the place they work (commercial). Despite this massive expense, many users are dissatisfied with the thermal conditions in buildings.
In this thesis, we explore the tradeoff between commercial building HVAC energy consumption and the quality of thermal conditioning provided to users. The framework has several components that help to address the current HVAC control systems shortcomings, including (a) occupancy sensing in real-time (b) occupancy prediction models based on historical occupancy data (c) human-in-the-loop comfort feedback (d) data-driven thermodynamic building models, and (e) weather forecasting data
All these components provide the necessary input to our model predictive control optimization framework that minimizes monetary costs in energy use while maintaining quality comfort bounds for the building's users based on real-time user's feedback. We tested our framework OFFICE in a real LEED Gold certified university building with over 20 workers performing their daily tasks for 4 weeks, and we showed that we could obtain monetary costs savings of more than 10% while at the same time reducing the users' dissatisfaction levels with thermal comfort from 25% to 0% dissatisfaction, significantly improving the quality of thermal service provided to the building's users.