Digital electricity meters – also referred to as smart meters – measure the total electricity consumption of a household at a fine temporal granularity. Using this data, detailed information like the consumption of individual appliances can be retrieved and used to provide novel services, such as personalized energy consulting. In this paper, we build upon existing work in consumption data disaggregation by enriching smart meter data with additional information made available by networked sensors and household appliances. In particular, we investigate the use of ON/OFF events, which signal when appliances have been turned on or off, respectively. We analyze the performance of an existing algorithm that uses such events along with smart meter data to estimate the consumption of single appliances. Our results, obtained by applying the algorithm to a publicly available dataset, show that the accuracy of the algorithm quickly deteriorates as the number of available ON/OFF events decreases. We thus suggest possible countermeasures to cope with this limitation and to provide accurate electricity consumption breakdowns in private households.