Deep Reinforcement Learning based Optimal Control of Hot Water Systems
This addresses energy efficiency in domestic hot water systems, which is a significant global energy draw, with potential for broad replication and multi-Gigawatt-hour scale savings.
The paper tackles the problem of optimizing energy consumption for hot water production in buildings by developing a reinforcement learning-based method that learns occupant preferences and system dynamics on the fly, achieving a 20% reduction in energy consumption with no loss of comfort in a test on 32 houses in the Netherlands.
Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. These assumptions lead to suboptimal energy efficiency in the real world. In this paper, we present a novel reinforcement learning based methodology which optimizes hot water production. The proposed methodology is completely generalizable, and does not require an offline step or human domain knowledge to build a model for the hot water vessel or the heating element. Occupant preferences too are learnt on the fly. The proposed system is applied to a set of 32 houses in the Netherlands where it reduces energy consumption for hot water production by roughly 20% with no loss of occupant comfort. Extrapolating, this translates to absolute savings of roughly 200 kWh for a single household on an annual basis. This performance can be replicated to any domestic hot water system and optimization objective, given that the fairly minimal requirements on sensor data are met. With millions of hot water systems operational worldwide, the proposed framework has the potential to reduce energy consumption in existing and new systems on a multi Gigawatt-hour scale in the years to come.