Generalizable occupant-driven optimization model for domestic hot water production in NZEB. (1st August 2016)

• Record Type: Journal Article
• Authors: Kazmi, H.; D'Oca, S.; Delmastro, C.; Lodeweyckx, S.; Corgnati, S.P.
• Is Part Of: Applied energy. Volume 175(2016)
• Page Start: 1
• Page End: 15
• Publication Date: 2016-08-01
• Subjects: Domestic hot water (DHW) -- Reinforcement learning -- Optimal control -- Occupant behaviour -- NZEB -- Heat pumps; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.04.108
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Smart meter data for domestic hot water consumption is collected for 46 NZEB. Reinforcement learning optimizes energy consumed while constrained on user comfort. Online optimization models learn occupant behaviour and system thermodynamics. Offline generalizable models calibrate dynamically the storage vessel operation. Real world application of the active controls resulted in energy savings of 27%. 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 level. 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 kW h). 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.