TY - GEN
T1 - Robust Non-Intrusive Load Monitoring (NILM) with unknown loads
AU - Welikala, Shirantha
AU - Dinesh, Chinthaka
AU - Godaliyadda, Roshan Indika
AU - Ekanayake, Mervyn Parakrama B.
AU - Ekanayake, Janaka
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households.
AB - A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households.
KW - Appliance Identification
KW - Demand Side Management (DSM)
KW - Non-Intrusive Load Monitoring (NILM)
KW - Smart Grid
KW - Smart Meters
KW - Unknown Appliances
KW - User side
UR - http://www.scopus.com/inward/record.url?scp=85021918078&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021918078&partnerID=8YFLogxK
U2 - 10.1109/ICIAFS.2016.7946569
DO - 10.1109/ICIAFS.2016.7946569
M3 - Conference contribution
AN - SCOPUS:85021918078
T3 - 2016 IEEE International Conference on Information and Automation for Sustainability: Interoperable Sustainable Smart Systems for Next Generation, ICIAfS 2016
BT - 2016 IEEE International Conference on Information and Automation for Sustainability
T2 - 8th IEEE International Conference on Information and Automation for Sustainability, ICIAfS 2016
Y2 - 16 December 2016 through 19 December 2016
ER -