REDE NEURAL CONVOLUCIONAL AUTOCONFIGURADA PARA
IDENTIFICAÇÃO DE CARGAS ELÉTRICAS SIMILARES EM SMART
GRID
Name: VINICIUS WITTIG VIANNA
Publication date: 29/03/2021
Advisor:
Name |
Role |
|---|---|
| WANDERLEY CARDOSO CELESTE | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| DANIEL JOSÉ CUSTODIO COURA | Advisor * |
| HELDER ROBERTO DE OLIVEIRA ROCHA | Advisor * |
| WANDERLEY CARDOSO CELESTE | Advisor * |
Summary: Convolutional Neural Networks (CNN) have been shown to be very efficient tools in the task
of identifying similar electrical charges in a smart grid. However, these networks currently
depend on highly skilled labor to be properly designed, in view of the large number of
hyperparameters and variety of adjustments, which makes this undertaking highly laborious
and costly. In addition, traditionally manual and completely empirical adjustments do not
guarantee the achievement of an optimal architecture, due to the impossibility, in general, of
testing all adjustment combinations for a set of hyperparameters, within a previously defined
value space. Therefore, this work has as main objective to add an automated adjustment
mechanism of the hyperparameters of a CNN dedicated to the autonomous identification of
highly similar electrical charges in a smart grid. For this purpose, a classification system based
on a CNN architecture manually obtained from previous work is initially used, in order to find
the minimum number of necessary and sufficient cases to strategically allow a classification
accuracy of at least 95%. Then, the optimum number of cases is used to optimize the number
of CNN convolutional and dense layers, in addition to the number of neurons in such layers,
without compromising the performance of the reference architecture (the manually adjusted
one). The system was tested using two sets of data, one based on arrays of up to four technically
identical fluorescent lamps and the other based on arrays of up to four microcomputers also
technically identical. With the first set, the reduction in the number of cases required for training
the reference CNN was 90%, while in the second case, it was 33.4%. Then, the respective
minimum number of cases were used to adjust hyperparameters from the reference CNN,
resulting in a reduction of 56.02% and 90.41% over the number of trainable parameters of such
networks, from the respective databases.
Keywords: NILM. Automated Machine Learning. Heuristic Optimization. Computational
Cost.
