|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4914084||1362869||2018||5 صفحه PDF||ندارد||دانلود کنید|
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 voltageâcurrent trajectory. In this paper, a weighted pixelated image of the voltageâcurrent 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.
Journal: Energy and Buildings - Volume 158, 1 January 2018, Pages 32-36