We propose two algorithms for power load disaggregation at low-sampling rates (greater than 1sec): a low-complexity, supervised approach based on Decision Trees and an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. We provide reproducible algorithmic description and benchmark the proposed methods with a state-of-the-art Hidden Markov Model (HMM)-based approach. Experimental results using three US and three UK households, show that both proposed methods outperform the HMM-based approach and are capable of disaggregating a range of domestic loads even when the training period is very short.