Data anonymization is crucial to allow the widespread adoption of some technologies, such as smart meters. However, anonymization techniques should be evaluated in the context of a dataset to make meaningful statements about their eligibility for a particular use case. In this paper, we therefore analyze the suitability of continuous k_s-anonymization with CASTLE for data streams generated by smart meters. We compare CASTLE ’s continuous, piecewise k_s-anonymization with a global process in which all data is known at once, based on metrics like information loss and properties of the sensitive attribute. Our results suggest that continuous k_s-anonymization of smart meter data is reasonable and ensures privacy while having comparably low utility loss.