BUILDING AN ADAPTIVE HYBRID MODEL FOR SHORT-TERM PREDICTION OF POWER CONSUMPTION USING A NEURAL NETWORK

Ibrayeva G. Bulatbayeva Y. Sarsikeyev Y.
2022 Technology Center

Eastern-European Journal of Enterprise Technologies
2022 #2 Issue 8-116 6 - 12 pp.

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 Copyright

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

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Department of Automation of Manufacturing Processes, Karaganda Technical University, N. Nazarbayev ave., 56, Karaganda, 100027, Kazakhstan
Department of Electrical Equipment Operation, S. Seifullin Kazakh Agro Technical University, Zhenis ave., 62, Nur-Sultan, 010011, Kazakhstan

Department of Automation of Manufacturing Processes
Department of Electrical Equipment Operation

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