Deep Reinforcement Learning based Optimal Control of Hot Water Systems

TL;DR

This paper introduces a reinforcement learning approach for optimizing hot water systems that learns occupant preferences and system dynamics in real-time, significantly reducing energy use without sacrificing comfort.

Contribution

It presents a novel RL-based method that does not require prior system modeling or offline training, enabling real-time, occupant-aware hot water system optimization.

Findings

Reduces hot water energy consumption by ~20% in tested homes

Learns occupant preferences on the fly without prior data

Potential to save hundreds of kWh annually per household

Abstract

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.