In today’s society, a current concern is to mitigate the risks of global climate change. Throughout the years there have been several initiatives to achieve more sustainable energy distribution in buildings. In this work, a new methodology is proposed for identifying appliance consumption patterns in buildings. It consists of, at first, conducting a seasonality analysis based on the Auto-Correlation Function for detecting the different appliance use patterns that arise in a given time window. Then, it is conducted a Probability Distribution Analysis based on the auto-correlation results and the calculation of an informative measure to select the prevailing consumption pattern. The methodology enables to distinguish between different use patterns for a given appliance for each building at specific time intervals, e.g., the seasons of the year. For the purpose of illustration, the methodology is applied to consumption data of four appliances selected from a domestic energy consumption dataset (REFIT) over one year. The results provide several insights on how a given appliance use evolves throughout the seasons for each household, and also highlighting use similarity for different appliances across the seasons. These results would be, otherwise, hidden away, and would require an individual analysis of consumption patterns of each appliance. Consequently, the methodology provides a consistent mechanism to identify different user profiles.