CONSTRUCTION OF A RECURRENT NEURAL NETWORK-BASED ELECTRICAL LOAD FORECASTING MODEL FOR A 110 kV SUBSTATION: A CASE STUDY IN THE WESTERN REGION OF THE REPUBLIC OF KAZAKHSTAN
Kenessov Y. Tokhtibakiev K. Saukhimov A. Vassilyev D. Gunin A. Iliyasov A.
2024Technology Center
Eastern-European Journal of Enterprise Technologies
2024#2Issue 8(128)6 - 15 pp.
This paper presents an approach for using a long short-term memory (LSTM)-based recurrent neural network with various configurations to construct a forecasting model for electrical load prediction of a 110 kV substation. The issues of unbalances arising in energy management systems due to discrepancies between generated and consumed energy can lead to power outages and blackouts. With the introduction of the most accurate forecasts, the task of maintaining electrical reliability for grid operators can be greatly simplified. Through an investigation into 81 different parameter combinations the research revealed the optimal setup for an LSTM model in the task of electrical load forecasting. This configuration comprised 15 neurons, a batch size of 16, and employed the Adamax optimization algorithm. Applying this specific setup yielded a mean squared error (MSE) of 5.584 MW2 and an R2 value of 0.99. High R2 values and low MSE values indicate that the LSTM model accurately captures changes in electricity consumption with minimal deviation between predicted and actual consumption values. Selection of appropriate parameters significantly enhances the performance of the predictive model and resulted in a reduction of the MSE error from 12.706 to 5.584 MW2. The optimized configuration of the LSTM model, tailored through extensive experimentation, enhances its predictive capabilities. The proposed LSTM model holds practical utility for integrating into systems for monitoring and forecasting mode reliability of electrical networks, particularly in the Western energy hub of the Republic of Kazakhstan. Its accuracy and reliability make it valuable for energy resource management and infrastructure planning Copyright
long short-term memory-basedloadforecasting , recurrent neural networks , short-term load forecasting
Text of the article Перейти на текст статьи
Technical Expert of Project Development and Digital Transformation, KazNIPI Energoprom JSC, Abylai Khan ave., 58А, Almaty, 050004, Kazakhstan
Department of Electric Power Systems, Almaty University of Power Engineering and Telecommunications, named after Gumarbek Daukeyev, Baytursynuli str., 126/1, Almaty, 050013, Kazakhstan
Technical Expert of Project Development and Digital Transformation
Department of Electric Power Systems
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026