Abstract Electricity consumption in the United Kingdom is continually growing with demand from the domestic sector a potential/major contribution to this increase in consumption. Although demand is increasing, little information exists on the domestic components that contribute to an increase in domestic energy consumption. Thus, a greater understanding on what is contributing to the increase in domestic energy usage is a pre-requisite to understand how it can be reduced in the future or, if not reduced, contained at its current level. This article discusses a separation filter designed for disaggregating domestic electrical demand data into different appliance categories. The filter is applied to a real time domestic electrical dataset spanning 1 year, and trends in standby, cold, heating element spikes and residual demand are identified. Several reasons to account for each of the trends are discussed. Additionally, the filter is applied to synthetic data both to confirm the accuracy of the separation filter and to finely adjust the filter for future application. The results indicate an increase in occupancy-related demand consumption during the winter months and an increase in cold consumption during the summer months. Furthermore, the results demonstrate that in contrast to changes observed in occupancy-related demand and cold consumption, there is little variation in standby and heating element spike consumption throughout the year. Finally, the potential advantage of incorporating a tailored separation filter into domestic smart meters is discussed.