Residential Heat Pump for HVAC and DHW with Predictive Controls
UC Davis is developing a control system that uses model predictive controls to optimize heat pump operation for cost, comfort, and carbon emissions. As part of CalFlexHub, this work is getting expanded to work with real-time electricity price signals. The control works as a supervisor control for heat pump space conditioning systems or water heaters that have an existing API which enables communication with the cloud. The control algorithm sends the on-board control system a new setpoint schedule every 15 minutes based on the objective of satisfying comfort while reducing cost, carbon emissions or both. The control system is being developed as part of a modular control framework (data model framework) to allow two different heat pump systems to be controlled using the same framework and minimal modifications.
Lawrence Berkeley National Laboratory (Berkeley Lab) is a Department of Energy (DOE) sponsored Office of Science laboratory and a lead with the Energy Efficiency and Renewable Energy’s (EERE) Buildings Technology Office (BTO), providing state, national, and global leadership in the development of interoperable and load flexible technologies, including grid-interactive efficient buildings (GEB) and Connected Communities.
CalFlexHub is made possible by the California Energy Commission and funded through the Electric Program Investment Charge (EPIC) Program.
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