Abstract We address the problem of forecasting the usage of multiple electrical appliances by domestic users, with the aim of providing suggestions about the best time to run appliances in order to reduce carbon emissions and save money (assuming time-ofuse pricing), while minimising the impact on the users’ daily habits. An important challenge related to this problem is the modelling the everyday routine of the consumers and of the inter–dependencies between the use of different appliances. Given this, we develop an important building block of future home energy management systems: a prediction algorithm, based on a graphical model, that captures the everyday habits and the inter–dependency between appliances by exploiting their periodic features. We demonstrate through extensive empirical evaluations on real–world data from a prominent database that our approach outperforms existing methods by up to 47%.