Due to the increasing cost of electricity and its variable price structure throughout the day, it is of interest
to shift the loads to off-peak hours. In this work, the grey-box model of a domestic hot water electric
boiler is presented. The developed model is useful for the development of new supervisory controller to
help offset the boiler heating load to off peak hours in a smart grid environment. The boiler used in this
research is an integral part of the domestic hot water system and residential Heating, Ventilating and AirConditioning
(HVAC) systems in many Swiss homes. The water stored in the boiler is not well mixed and
thus the temperature varies along the height of the boiler. The cold water is entering in the boiler from
the bottom and the hot water is drawn at the top. This results in a temperature gradient along the height
of the boiler which needs to be predicted to accurately simulate the temperature dynamics of the boiler.
The boiler was divided into eight stratified virtual layers and physics-based model was developed by
writing the heat balance equation for each layer. Experimental setup consisting of boiler, sensors and
data logger was prepared at the Institute of Aerosol and Sensor Technology (IAST), University of Applied
Sciences and Arts Northwestern Switzerland (FHNW) to measure the training and test data for the model
including the temperature of each layer, ambient temperature, boiler's power consumption and flow rate
of water entering into the boiler. The parameters of the physics-based model were estimated from the
measured data thus converting it into a grey-box model. The model performance was visually compared
to the measured data and was also evaluated analytically using several metrics showing the high accuracy
of the developed model.