Non-Intrusive Load Monitoring using Feed Forward Neural Network

Dhivya Sampath Kumar, K. L. Low, Anurag Sharma, Wai Lok Woo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)


In recent years, electricity demands have increased because of the growing population. In order to reduce energy consumption, several studies have concluded that Non-Intrusive Load Monitoring (NILM) is effective in raising awareness for users to monitor their daily energy consumption which is beneficial for energy conservation. NILM is a technique that monitor and analyze energy usage through load measurements. These load measurements are used for examining appliances power consumption behavior and the data can be used to modify habits of users through utility bills. This paper proposes a feed-forward neural network approach for NILM using magnitude of current harmonics for load identifications. Experiments for steady state and transient state waveform were first conducted to acquire individual signature current harmonics of appliances and the data acquire are being fed into the neural network for training and later used for load identification. The results concluded that data collected from steady state are better and the simulation results reveal that the proposed neural network approach was able to identify the appliances accurately.

Original languageEnglish
Title of host publication2019 IEEE PES Innovative Smart Grid Technologies Asia
Subtitle of host publicationISGT 2019
Editors IEEE
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728135205
Publication statusPublished - 1 May 2019
Event2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 - Chengdu, China
Duration: 21 May 201924 May 2019

Publication series

Name2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019


Conference2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019


Dive into the research topics of 'Non-Intrusive Load Monitoring using Feed Forward Neural Network'. Together they form a unique fingerprint.

Cite this