DocumentCode :
2629287
Title :
Appliance usage prediction using a time series based classification approach
Author :
Basu, Kaustav ; Debusschere, Vincent ; Bacha, Seddik
Author_Institution :
Grenoble Electr. Eng. Lab. (G2E Lab.), St. Martin d´´Hères, France
fYear :
2012
fDate :
25-28 Oct. 2012
Firstpage :
1217
Lastpage :
1222
Abstract :
Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement(with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.
Keywords :
building management systems; data mining; decision trees; energy consumption; energy management systems; pattern classification; power engineering computing; time series; appliance usage prediction; data mining; decision tree; energy consumption data; energy management; residential homes; time 1 hour; time series based classification approach; time-series based multilabel classifier approach; Accuracy; Bayesian methods; Europe; Load modeling; Microwave measurements; Ovens; Appliance Usage Prediction; Data Mining; Energy Management in Homes; Learning Algorithm; Multi-label classifiers; Smart-Buildings; decision tree;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society
Conference_Location :
Montreal, QC
ISSN :
1553-572X
Print_ISBN :
978-1-4673-2419-9
Electronic_ISBN :
1553-572X
Type :
conf
DOI :
10.1109/IECON.2012.6388597
Filename :
6388597
Link To Document :
بازگشت