A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A prediction system for home appliance usage
Highlights ► The prediction of appliance usage in smart homes is done, as an important problem in the home energy management. ► A global architecture for prediction with an oracle module and a predictor module was proposed. ► Several classifiers were tested as predictors and for each tested appliance, the best one was detected. ► The prediction with decision tables for lighting usage in dwellings gives the highest accuracy in all the tested cases. ► The oracle module provides additional information which leads to more accurate prediction.
Abstract 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 24h 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 24h.
A prediction system for home appliance usage
Highlights ► The prediction of appliance usage in smart homes is done, as an important problem in the home energy management. ► A global architecture for prediction with an oracle module and a predictor module was proposed. ► Several classifiers were tested as predictors and for each tested appliance, the best one was detected. ► The prediction with decision tables for lighting usage in dwellings gives the highest accuracy in all the tested cases. ► The oracle module provides additional information which leads to more accurate prediction.
Abstract 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 24h 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 24h.
A prediction system for home appliance usage
Basu, Kaustav (author) / Hawarah, Lamis (author) / Arghira, Nicoleta (author) / Joumaa, Hussein (author) / Ploix, Stephane (author)
Energy and Buildings ; 67 ; 668-679
2013-02-01
12 pages
Article (Journal)
Electronic Resource
English