In 2023, Germany’s net electricity consumption amounted to 467 TWh, with households accounting for 28%. Their active participation—alongside industry—is key to advancing the energy transition. As part of a research project, a web application was developed to process and transparently visualize household energy data using artificial intelligence (AI), helping users monitor and evaluate their consumption behavior. The goal is to encourage long-term engagement in the energy transition. This paper presents the utilization of artificial intelligence to identify the energy consumption of individual appliances through the disaggregation of smart meter data of the households. Three households of a row-house complex with different consumption behavior were selected as examples for the analysis. A distinction was made between the flexible and non-flexible loads. It was shown that the non-flexible share of household electricity consumption can be determined with an accuracy over 90%. Therefore, household residents can be provided with useful information on this consumption share with minimal effort and without additional measurements just by using the smart meter data, as it is planned as part of the smart meter rollout. Flexible consumption, such as from washing machines and dishwashers, was harder to detect due to infrequent use and low temporal resolution. Combining the consumption of several flexible appliances improved the results from 53.9% to 72% disaggregation accuracy across all households and appliances.