@inproceedings{f79de35a835843a1bd693256bfb5d9d3,
title = "Non-Intrusive Load Monitoring using Electricity Smart Meter Data: A Deep Learning Approach",
abstract = "Non-Intrusive Load Monitoring (NILM) is the process of decomposing an aggregated building electricity mains measurement into individual appliances. NILM is a very challenging classification problem and a number of statistical techniques have been proposed for this. Recent advances have made deep learning a dominant approach for classification in fields such as image processing and speech recognition. This paper investigates the application of deep learning approaches in NILM, and develops a NILM classifier that can detect the activations of common electrical appliances from smart meter data. The performance of the NILM deep learning classifier is demonstrated using publicly- available smart meter data sets, and the ability of the classifier to generalise to unseen data is examined.",
keywords = "appliance identification, energy disaggregation, load disaggregation, machine learning, Non-intrusive load monitoring, smart metering",
author = "Michael Devlin and Hayes, \{Barry P.\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 ; Conference date: 04-08-2019 Through 08-08-2019",
year = "2019",
month = aug,
doi = "10.1109/PESGM40551.2019.8973732",
language = "English",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2019 IEEE Power and Energy Society General Meeting, PESGM 2019",
address = "United States",
}