Inside and out: A Platform to Characterise Stratification in Horizontal Electric Water Heaters

Abstract

Electric water heaters are a large contributor to domestic electrical energy dissipation and contribute disproportionally to the domestic load during peak grid hours. However, their capacity to store energy makes them well suited to demand-side management. However, poor application thereof could come at the cost of energy savings, user comfort, and health risks. Ther-mal models for simulation of water heaters - used in planning and smart control - therefore need to accurately reflect what happens inside the water heater, while being computationally efficient. Validating the models are heavily dependent on usage profiles, environmental conditions, and heating schedules or electrical availability. Some validation has been done for vertical water heaters, but limited work exists for horizontal water heaters. Moreover, none have been validated in the environmental and user envelope. In this work we propose a platform to fully characterise stratification in horizontal water heaters, which includes full user and environmental emulation. The platform will be used to validate existing models, but also to train and assess machine learning models.