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16 March 2023 Prediction and clustering models based on multivariate parameters
YingLan Fang, Qi Sun, PengFei Zhang
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Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 1259305 (2023) https://doi.org/10.1117/12.2671657
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China
Abstract
In the multi-parameter sequence in the industrial electrolyzer, in order to solve the problem that the traditional method is difficult to predict the nonlinear features and obtain the hidden feature information in the sequence, this paper uses the VARMA model to fit the multi-parameter features and combines the Time2Vec vector to embed the time form as the neural network. Augmented data sources for automated feature engineering and generalization of deep learning techniques; multivariate parameters were dimensionally reduced and KS tests were used to capture correlations in order to explore relationships between electrolyzers. The experimental results show that the model is superior to other comparative models in terms of computational efficiency, accuracy, and network structure, which verifies the effectiveness of its prediction in the multi-parameter field.
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YingLan Fang, Qi Sun, and PengFei Zhang "Prediction and clustering models based on multivariate parameters", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 1259305 (16 March 2023); https://doi.org/10.1117/12.2671657
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KEYWORDS
Neural networks
Aluminum
Data modeling
Deep learning
Engineering
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