This thesis presents an advanced agent-based model that accurately mimics household energy and water consumption patterns to enable future studies in the energy transition context. Leveraging data from census and national energy use surveys, the model generates a synthetic population that mirrors the statistical characteristics of real demographics. Each household within this population is assigned a specific set of appliances, informed by real-world usage data, to simulate daily and weekly consumption behaviours. The model captures the nuanced variations in energy and water use across different times of day and types of days, i.e. weekdays and weekends, at the level of the single appliance.