Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting

Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological...

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Bibliographic Details
Main Authors:	Pavel V. Matrenin, Vadim Z. Manusov, Alexandra I. Khalyasmaa, Dmitry V. Antonenkov, Stanislav A. Eroshenko, Denis N. Butusov
Format:	Article
Language:	English
Published:	MDPI AG 2020-12-01
Series:	Mathematics
Subjects:	coal mining neural network applications recurrent neural networks short-term load forecasting
Online Access:	https://www.mdpi.com/2227-7390/8/12/2169

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