TY - GEN
T1 - Automatic label correction and appliance prioritization in single household electricity disaggregation
AU - Valovage, Mark
AU - Gini, Maria
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Electricity disaggregation focuses on classification of individual appliances by monitoring aggregate electrical signals. In this paper we present a novel algorithm to automatically correct labels, discard contaminated training samples, and boost signal to noise ratio through high frequency noise reduction. We also propose a method for prioritized classification which classifies appliances with the most intense signals first. When tested on four houses in Kaggles Belkin dataset, these methods automatically relabel over 77% of all training samples and decrease error rate by an average of 45% in both real power and high frequency noise classification.
AB - Electricity disaggregation focuses on classification of individual appliances by monitoring aggregate electrical signals. In this paper we present a novel algorithm to automatically correct labels, discard contaminated training samples, and boost signal to noise ratio through high frequency noise reduction. We also propose a method for prioritized classification which classifies appliances with the most intense signals first. When tested on four houses in Kaggles Belkin dataset, these methods automatically relabel over 77% of all training samples and decrease error rate by an average of 45% in both real power and high frequency noise classification.
UR - http://www.scopus.com/inward/record.url?scp=85021989614&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021989614&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85021989614
T3 - AAAI Workshop - Technical Report
SP - 262
EP - 269
BT - WS-16-01
PB - AI Access Foundation
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -