Generalizable occupant-driven optimization model for domestic hot water production in NZEB

Abstract

The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water (DHW) production in residential buildings. This is done by deriving data-driven optimal heating schedules (used interchangeably with policies) automatically. The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism -an air-source heat pump (ASHP) in this case -at the highest possible efficiency. The proposed algorithm, while tested on an ASHP, is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well. Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings (NZEB) in the Netherlands are presented. These show a reduction of energy consumption for DHW by 20% using a computationally inexpensive heuristic approach, and 27% when using a more intensive hybrid ant colony optimization based method. The energy savings are strongly dependent on occupant comfort. This is demonstrated in real world settings for a low-consumption house where active control was performed using heuristics for 3.5 months and resulted in energy savings of 27% (61 kWh). It is straightforward to extend the same models to perform automatic demand side management (ADSM) by treating the DHW vessel as a flexibility bearing device.

Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

“…Both papers are relevant to this work, although here the scope is narrower: not the building but the solar DHW system alone. Kazmi et al [47] proposed a solution for this problem employing a set of tools including a hybrid reinforcement learning process, seasonal auto-regressive integrated mobile average (SARIMA) model, and a hybrid ant-colony optimization (hACO) algorithm. The paper contains useful remarks about thermal energy storage modelling.…”

Smart Home Futures: Algorithmic Challenges and Opportunities

“…In the context of smart homes, this means agents which observe the state of the smart home, the building and the occupant and then reason about possible next actions which can then be executed (a practical example of such an approach appears in [20]). Model-based reinforcement learning includes an additional step, whereby the controller first learns a representation of the environment, which it then uses to reason about next actions (a practical example of such an approach appears in [21]). These differences are elaborated on in Fig.…”

“…The energy consumption can be for any end draw, e.g. for thermal conditioning of the building or to providing adequate lighting under all possible conditions [15], [18], [21]. It can also be extended to other use cases such as minimizing the amount of water being consumed by informing the user of any leaks or the fact that a dishwasher might save them additional water.…”

Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

“…Although an increased efficiency of the primary equipment can decrease the amount of energy consumption in a hot water system, this decrease often creates a bottleneck in reality, when most of the equipment has been updated to achieve better efficiency . Most people tend to neglect the energy saving potential during an operation period, despite the fact that a significant amount of energy can still be saved.…”

“…12 Although an increased efficiency of the primary equipment can decrease the amount of energy consumption in a hot water system, this decrease often creates a bottleneck in reality, when most of the equipment has been updated to achieve better efficiency. 13 Most people tend to neglect the energy saving potential during an operation period, despite the fact that a significant amount of energy can still be saved. A study from the Netherlands revealed that if the occupants do not conduct their tasks in a highly efficient way, that is, one that supports the intended design, even a high-performance system can consume much more energy than expected.…”