![]() ![]() iProgram: Inferring Smart Schedules for Dumb Thermostats. Srinivasan Iyengar, Sandeep Kalra, Anushree Ghosh, David E.In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on. Occupancy detection in commercial buildings using opportunistic context sources. Sunil Kumar Ghai, Lakshmi V Thanayankizil, Deva P Seetharam, and Dipanjan Chakraborty.SPOT: a smart personalized office thermal control system. Peter Xiang Gao and Srinivasan Keshav. ![]() OBSERVE: Occupancy-based System for Efficient Reduction of HVAC Energy. In Proceedings of the 2014 Conference on Designing Interactive Systems (DIS '14). Catch My Drift?: Achieving Comfort More Sustainably in Conventionally Heated Buildings. Adrian Clear, Adrian Friday, Mike Hazas, and Carolynne Lord.ThermoSense: Occupancy Thermal Based Sensing for HVAC Control. Optimal HVAC Building Control with Occupancy Prediction. Sentinel: Occupancy Based HVAC Actuation Using Existing WiFi Infrastructure Within Commercial Buildings. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments. Non-Intrusive Techniques for Establishing Occupancy Related Energy Savings in Commercial Buildings. Omid Ardakanian, Arka Bhattacharya, and David Culler.Occupancy-Driven Energy Management for Smart Building Automation. Duty-cycling Buildings Aggressively: The Next Frontier in HVAC Control. Compared to the current static approach, our results demonstrate that learning HVAC schedules from mobile WiFi activity across the campus can yield a 37% reduction in waste time, a measure of energy savings, and a 3% reduction in miss time, a measure of user comfort. Our analysis reveals significant differences in occupancy patterns across and within buildings, motivating the need for our adaptive learning-based approach. To evaluate our techniques, we analyze data from several thousand WiFi access points deployed in 112 office buildings on a university campus. Our approach is adaptive and dynamically adjusts its schedules as occupancy patterns change, much like a learning thermostat. We analyze building WiFi activity, specifically from smartphones, to infer detailed spatial occupancy patterns in each building, and present an algorithm that learns from these patterns to derive a custom HVAC schedule. While our technique is compatible with any occupancy sensor, we leverage the existing wireless networking infrastructure that is omnipresent across any modern campus. In this paper, we propose a novel Machine Learning-driven technique to automatically learn custom occupancy-based HVAC schedules for buildings across a large campus. While "smart" HVAC technologies, such as learning thermostats, are widely available for residential use, commercial buildings typically rely on legacy systems that are difficult to upgrade and require facility managers to manually set HVAC schedules. Heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the energy consumed by commercial buildings.
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