TY - GEN
T1 - Solar panel analysis with adversary neural networks
AU - Muuoz-Ccuro, Felipa Elvira
AU - Mamani, Mercy Jeaninna Alvarado
AU - Hernandez, Victor Daniel Hijar
AU - Del Carmen Emilia Ancaya-Martinez, Maria
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/5
Y1 - 2021/8/5
N2 - When solar panels received the irradiance from the sun, early detection is important to prevent fault or fast degradation. This research article provides a new method using 'Generative Adversary Neural Networks' [1] (GANN), with a deep learning techniques, for evaluation of the degradation in solar panels (SP). The methodology required root cause analysis for SP degradation, it considered four stages for the deep learning: 'preprocessing, segmentation, extraction, and classification' [11]. In this paper, we are determined artificial intelligence methodology and new neural network proposal for panel degradation detection based on root cause analysis [2]. The effectiveness of the results were 97.5%; with minimum information. However, the training process produces 0.105 % false positives.
AB - When solar panels received the irradiance from the sun, early detection is important to prevent fault or fast degradation. This research article provides a new method using 'Generative Adversary Neural Networks' [1] (GANN), with a deep learning techniques, for evaluation of the degradation in solar panels (SP). The methodology required root cause analysis for SP degradation, it considered four stages for the deep learning: 'preprocessing, segmentation, extraction, and classification' [11]. In this paper, we are determined artificial intelligence methodology and new neural network proposal for panel degradation detection based on root cause analysis [2]. The effectiveness of the results were 97.5%; with minimum information. However, the training process produces 0.105 % false positives.
KW - Deep Learning
KW - generative adversary
KW - neural network
KW - root cause analysis
UR - https://www.scopus.com/pages/publications/85116296627
U2 - 10.1109/INTERCON52678.2021.9532698
DO - 10.1109/INTERCON52678.2021.9532698
M3 - Conference contribution
AN - SCOPUS:85116296627
T3 - Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
BT - Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
Y2 - 5 August 2021 through 7 August 2021
ER -