  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.
