Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4914084 | Energy and Buildings | 2018 | 5 Pages |
Abstract
Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analysing changes in the voltage and current measured at the grid connection point of the household. The goal is to identify the active appliances, based on their unique fingerprint. An informative characteristic to attain this goal is the voltage-current trajectory. In this paper, a weighted pixelated image of the voltage-current trajectory is used as input data for a deep learning method: a convolutional neural network that will automatically extract key features for appliance classification. The macro-average F-measure is 77.60% for the PLAID dataset and 75.46% for the WHITED dataset.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Leen De Baets, Joeri Ruyssinck, Chris Develder, Tom Dhaene, Dirk Deschrijver,