Model Predictive Control of Heat Pump Water Heaters for Energy Efficiency

Water heating accounts for 17.7% of total residential energy use in the United States and is the second largest end use after space heating and cooling. There is great potential to improve the energy efficiency of residential water heaters and reduce their energy use in new and existing homes. Previous efforts primarily focused on improving the insulation and combustion efficiency of water heaters, whereas little effort has been made from the control perspective. Heat pump water heaters (HPWHs) provide an energy efficient solution for water heating. Instead of generating heat directly, HPWHs transfer heat from the environment into the water in the tank. The heat pump is two to three times more energy efficient than resistance elements, although HPWHs typically include elements for backup and high demand situations. We propose a model predictive control (MPC) framework that aims to achieve maximum energy savings while maintaining thermal comfort. This framework uses algorithms that automatically learn users’ hot water consumption patterns and adaptively increase the use of the heat pump to avoid resistance element use. It has been tested through simulations with hot water draw profiles collected from field tests. Simulation results indicate that this technique will save up to 20% for a low user or more than $20 per year.