Electricity disaggregation focuses on identifying individual appliances from one or more aggregate signals. By reporting detailed appliance usage to consumers, disaggregation has the potential to significantly reduce electrical waste in residential and commercial sectors. However, application of existing methods is limited by two critical shortcomings. First, supervised learning methods implicitly assume error- free labels in training data, an unrealistic expectation for imperfectly-labeled consumer data. Second, supervised and unsupervised learning methods require parameters to be tuned to individual appliances and/or datasets, limiting widespread application. To address these limitations, this paper introduces the implementation of Bayesian changepoint detection (BCD) with necessary adaptations to electricity disaggregation. We introduce an algorithm to effectively apply BCD to automatically correct labels. We then apply BCD to event detection to identify transitions between appliances' on and off states. Performance is evaluated using 3 publicly available datasets containing over 250 appliances across 11 houses. Results show both BCD applications are competi-Tive and in some cases outperform existing state-of-The-Art methods without the need for parameter tuning, advancing disaggregation towards widespread, real-world deployment.