TY - GEN
T1 - Improving energy disaggregation performance using appliance-driven sampling rates
AU - Schirmer, Pascal A.
AU - Mporas, Iosif
PY - 2019/9
Y1 - 2019/9
N2 - This paper proposes a new appliance-driven selection of sampling frequencies for improving the energy disaggregation performance in non-intrusive load monitoring. Specifically, the methodology uses a machine learning model with parallel device detectors and optimized device dependent sampling rates in order to improve device identification. The performance of the proposed methodology was evaluated on a state-of-the-art baseline system and a set of publicly available databases increasing performance up to 6.7% in terms of estimation accuracy when compared to the baseline energy disaggregation setup without device dependent sampling rates.
AB - This paper proposes a new appliance-driven selection of sampling frequencies for improving the energy disaggregation performance in non-intrusive load monitoring. Specifically, the methodology uses a machine learning model with parallel device detectors and optimized device dependent sampling rates in order to improve device identification. The performance of the proposed methodology was evaluated on a state-of-the-art baseline system and a set of publicly available databases increasing performance up to 6.7% in terms of estimation accuracy when compared to the baseline energy disaggregation setup without device dependent sampling rates.
KW - Device Classification
KW - Energy Disaggregation
KW - Non-intrusive load monitoring (NILM)
UR - http://www.scopus.com/inward/record.url?scp=85075596162&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO.2019.8902978
DO - 10.23919/EUSIPCO.2019.8902978
M3 - Conference contribution
AN - SCOPUS:85075596162
T3 - European Signal Processing Conference
BT - EUSIPCO 2019 - 27th European Signal Processing Conference
PB - European Signal Processing Conference, EUSIPCO
T2 - 27th European Signal Processing Conference, EUSIPCO 2019
Y2 - 2 September 2019 through 6 September 2019
ER -