Power management in homes and offices requires appliance usage prediction when the future user requests are not available. The randomness and uncertainties associated with an appliance usage make the prediction of appliance usage from energy consumption data a non-trivial task. A general model for prediction at the appliance level is still lacking. This work proposes to improve learning algorithms with expert knowledge and proposes a general model using a knowledge driven approach to forecast if a particular appliance will start during a given hour or not. The approach is both a knowledge driven and data driven one. The overall energy management for a house requires that the prediction is done for the next 24 h in the future. The proposed model is tested over the IRISE data and using different machine learning algorithms. The results for predicting the next hour consumption are presented, but the model works also for predicting the next 24 h. (C) 2013 Elsevier B.V. All rights reserved.