Non-Intrusive Load Monitoring using Electricity Smart Meter Data: A Deep Learning Approach
- 1 August 2019
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2019 IEEE Power & Energy Society General Meeting (PESGM)
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
This publication has 15 references indexed in Scilit:
- Neural NILMPublished by Association for Computing Machinery (ACM) ,2015
- Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter DataEnergies, 2015
- Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overviewPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homesScientific Data, 2015
- Revealing household characteristics from smart meter dataEnergy, 2014
- Non-intrusive appliance load monitoring using low-resolution smart meter dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- PALDi: Online Load Disaggregation via Particle FilteringIEEE Transactions on Instrumentation and Measurement, 2014
- Is disaggregation the holy grail of energy efficiency? The case of electricityEnergy Policy, 2013
- Smart meter data: Balancing consumer privacy concerns with legitimate applicationsEnergy Policy, 2012
- Nonintrusive appliance load monitoringProceedings of the IEEE, 1992