Energy disaggregation (or Non-Intrusive Load Monitoring (NILM)) is the process of deducing individual load profiles from aggregate measurements using different machine learning and pattern recognition tools. Existing disaggregation algorithms can be categorized into either supervised approaches or unsupervised ones. Supervised approaches require external information represented in either sub-metered loads or hand-labeled observations while unsupervised algorithms utilize only unlabeled aggregate data. We observed that very few works attempt to utilize both labeled and unlabeled data. In this paper, we introduce a semi-supervised learning tool, namely self-training, to the energy disaggregation problem. Semi-Supervised Learning (SSL) tools leverage both external and internal structural information in order to enhance the learning process and/or reduce the required labeling effort. We also provide test results of the utilized SSL tool compared with a traditional classification component of an event-based NILM system. Results show that even a simple SSL tool is able to reduce the required labeling effort and provides a learning disaggregation system whose performance gradually increases as it observes more unlabeled aggregate measurements.