Next Article in Journal
A Signal Complexity-Based Approach for AM–FM Signal Modes Counting
Next Article in Special Issue
PM2.5 Prediction Model Based on Combinational Hammerstein Recurrent Neural Networks
Previous Article in Journal
A Remark on Quadrics in Projective Klingenberg Spaces over a Certain Local Algebra
Previous Article in Special Issue
WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data Analytics
Open AccessArticle

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

1
Industrial Power Supply Systems Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia
2
Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
3
Power Plants Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia
4
Youth Research Institute, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(12), 2169; https://doi.org/10.3390/math8122169
Received: 28 October 2020 / Revised: 27 November 2020 / Accepted: 30 November 2020 / Published: 4 December 2020
(This article belongs to the Special Issue Applied Data Analytics)
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 process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset. View Full-Text
Keywords: coal mining; neural network applications; recurrent neural networks; short-term load forecasting coal mining; neural network applications; recurrent neural networks; short-term load forecasting
Show Figures

Figure 1

MDPI and ACS Style

Matrenin, P.V.; Manusov, V.Z.; Khalyasmaa, A.I.; Antonenkov, D.V.; Eroshenko, S.A.; Butusov, D.N. Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting. Mathematics 2020, 8, 2169. https://doi.org/10.3390/math8122169

AMA Style

Matrenin PV, Manusov VZ, Khalyasmaa AI, Antonenkov DV, Eroshenko SA, Butusov DN. Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting. Mathematics. 2020; 8(12):2169. https://doi.org/10.3390/math8122169

Chicago/Turabian Style

Matrenin, Pavel V.; Manusov, Vadim Z.; Khalyasmaa, Alexandra I.; Antonenkov, Dmitry V.; Eroshenko, Stanislav A.; Butusov, Denis N. 2020. "Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting" Mathematics 8, no. 12: 2169. https://doi.org/10.3390/math8122169

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop