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Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting

Welikala, S., Dinesh, C., Ekanayake, M P. B., Godaliyadda, R. I. and Ekanayake, Janaka 2017. Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting. IEEE Transactions on Smart Grid (99) 10.1109/TSG.2017.2743760

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This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identi- fication and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1949-3053
Date of First Compliant Deposit: 18 August 2017
Date of Acceptance: 18 August 2017
Last Modified: 08 Jan 2018 16:22

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