The HEMS runs a self-learning, predictive control algorithm that develops a thermal model of the home by observing the building’s behaviour in response to heat input, external weather conditions and other fac- tors. This thermal model is used to cost-optimize the operation of the participant’s heat pump. This means it should reach the specified target temperatures at the time periods set, but at lower cost than if the algorithm were not being applied. This is a complex, multidimen- sional trade-off as discussed by Carter, Lancaster, and Chanda (2017), who present modelled outputs from the algorithm.