Building an adaptive hybrid model for short-term prediction of power consumption using a neural network

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.254477

Keywords:

short-term forecasting, weighted average forecast, hybrid model, neural network, electrical load

Abstract

This paper proposes a step-by-step technique for combining basic models that forecast electricity consumption in an artificial neural network by the method of preliminary selection and further hybridization. The reported experiments were conducted using data on hourly electricity consumption at the metallurgical plant AO ArcelorMittal Temirtau in the period from January 1, 2019, to November 30, 2021. The current research is related to the planned introduction of a balancing electricity market. 96 combinations of basic models were compiled, differing in the type of neural network, the set of initial data, the order of lag, the learning algorithm, and the number of neurons in the hidden layer. It has been determined that the NARX-type network is the most optimal architecture to forecast electricity consumption. Based on experimental studies, the number of hidden neurons needed to form a planned daily profile should equal 3 or 4; it is recommended to use the conjugate gradient method as a learning algorithm. When selecting models from three groups, it was revealed that the conjugate gradient method produces better results compared to the Levenberg-Marquardt algorithm. It is determined that the values of the selected RMSE error indicator take values of 23.17, 22.54, and 22.56, respectively, for the first, second, and third data groups. The adaptive hybridization method has been shown to reduce the RMSE error rate to 21.73. However, the weights of the best models with values of 0.327 for the first group of data, and 0.336 for the second and third ones, show that the individual use of a separate combination of models is also applicable. The devised forecasting electricity consumption model can be integrated into an automated electricity metering system

Author Biographies

Gulnara Ibrayeva, Karaganda Technical University

Master of Engineering Science, Doctoral Student

Department of Automation of Manufacturing Processes

Yuliya Bulatbayeva, Karaganda Technical University

PhD, Acting Assistant Professor

Department of Automation of Manufacturing Processes

Yermek Sarsikeyev, S. Seifullin Kazakh Agro Technical University

PhD, Head of Department

Department of Electrical Equipment Operation

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Published

2022-04-30

How to Cite

Ibrayeva, G., Bulatbayeva, Y., & Sarsikeyev, Y. (2022). Building an adaptive hybrid model for short-term prediction of power consumption using a neural network . Eastern-European Journal of Enterprise Technologies, 2(8 (116), 6–12. https://doi.org/10.15587/1729-4061.2022.254477

Issue

Vol. 2 No. 8 (116) (2022): Energy-saving technologies and equipment

Section

Energy-saving technologies and equipment

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Copyright (c) 2022 Gulnara Ibrayeva, Yuliya Bulatbayeva, Yermek Sarsikeyev

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