CMU Researchers are using occupancy sensing and occupant feedback to drive a model predictive control (MPC) approach that targets 20% energy savings.
October 18, 2017
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Lead Performer: Carnegie Mellon University – Pittsburgh, PA
Partners:
-- Stony Brook University — Stony Brook, NY
-- Robert Bosch LLC — Pittsburgh, PA
DOE Total Funding: $1,223,986
Cost Share: $139,740
Project Term: June 1, 2016 – June 30, 2019
Funding Type: Building Energy Efficiency Frontiers and Innovations Technologies (BENEFIT) – 2016 (DE-FOA-0001383)
Project Objective
Occupant-centered control schemes can save energy by reducing unneeded space conditioning and lighting during unoccupied periods and by avoiding overly conservative operational settings when occupants are present in the space. Moving beyond the typically oversimplified representation of occupant comfort and actions (e.g., static group-level occupancy schedules and comfort proxies) will enable building control schemes that provide real-time feedback on individual-level occupant presence and/or comfort via a local sensing infrastructure. In this project, depth sensing will be used to produce fine grain estimates of occupancy. Along with occupant feedback, this data will be used to optimize a thermal comfort model as a means to match future space conditioning needs to the number of occupants and their preferences in order to reduce unnecessary energy consumption. 20% energy savings is targeted using a model predictive control (MPC) approach.
Contacts
DOE Technology Manager: Erika Gupta
Lead Performer: Mario Berges, Carnegie Mellon University