The energy sharing used to heat water represents around 15% in European houses. To improve energy efficiency, smart heating systems could benefit from accurate domestic hot water consumption forecasting in order to adapt their heating profile. However, forecasting the hot water consumption for a single accommodation can be difficult since the data are generally highly non smooth and present large variations from day to day. We propose to tackle this issue with three deep learning approaches, Recurrent Neural Networks, 1-Dimensional Convolutional Neural Networks and Multi-Head Attention to perform one day ahead prediction of hot water consumption for an individual residence. Moreover, similarly as in the transformer architecture, we experiment enriching the last two approaches with various forms of position encoding to include the order of the sequence in the data. The experimented models achieved satisfying performances in term of MSE on an individual residence dataset, showing that this approach is promising to conceive building energy management systems based on deep forecasting models.