Predicting Home Service Demands from Appliance Usage Data

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. In this work, we propose to enrich learning algorithms with expert knowledge and propose a general model using a knowledge driven approach to forecast if a particular appliance will start at 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 hours in the future. The proposed model is tested over the Irise data and the results are compared with some trivial knowledge driven predictors. Keywords-Appliance Usage Prediction, Enriched Learning Algorithm, Energy Management in Homes, Data Mining.