In this study, linear and non-linear models were trained on real data to mimic the relationship between household electricity consumption and indoor temperature, in the rooms of an apartment in downtown Stockholm. The aim was to better understand this relationship and to distinguish any divergence between the different rooms. With data from two weeks of measurements, the models proved to perform well when tested on validation data for almost all rooms, only showing performance dips for the middle room. A noticeable correlation between the electricity consumption and the indoor temperature was observed for all rooms except the bedroom. However, the benefits of using this information to predict the indoor temperature are limited and differ between the rooms. The household electricity consumption primarily brought beneficial information to the kitchen models, where most of the heat generating appliances were located. It was found that linear models were sufficient to represent the thermal systems of the rooms, performing equally well and often better than non-linear models.