recurrent neural networks, Kazakhstan, electrical consumption, forecasting system


The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan's electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships.

The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN's exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN's promise as a robust forecasting tool, pivotal for the energy sector's future resilience and efficiency.


Agrawal, R. K., Muchahary, F., & Tripathi, M. M. (2018). Long term load forecasting with hourly predictions based on long-short-term-memory networks. 2018 IEEE Texas Power and Energy Conference (TPEC). https://doi:10.1109/tpec.2018.8312088

Taheri, S., Jooshaki, M., & Moeini‐Aghtaie, M. (2021). Long-term planning of integrated local energy systems using deep learning algorithms. International Journal of Electrical Power & Energy Systems, 129, 106855.

Barzola-Monteses, J., Yánez-Pazmiño, W., Flores-Morán, E., & Bravo, F. P. (2022). Comparisons of Deep Learning Models to predict Energy Consumption of an Educational Building. 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT).

Jinjin, R., Wang, S., Wang, Z., & Mei, Y. (2019). Residential Energy Use Prediction across different Time Scales with Advanced Machine Learning Techniques. 2nd Asia Conference on Energy and Environment Engineering (ACEEE).

Nguyen, V. H., Ga, B. V., Kim, J., & Jang, Y. M. (2020). Power Demand Forecasting Using Long Short-Term Memory Neural Network based Smart Grid. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).

El-Taraboulsi, J., Cabrera, C., Roney, C., & Aung, N. (2023). Deep neural network architectures for cardiac image segmentation. Artificial Intelligence in the Life Sciences, 4, 100083.

Qiao, L., Li, Z., Xiao, B., Shu, Y., Wang, L., Shi, Y., Li, W., & Gao, X. (2023). QDRJL: Quaternion dynamic representation with joint learning neural network for heart sound signal abnormal detection. Neurocomputing, 562, 126889.

Gomes, R., Pham, T. D., He, N., Kamrowski, C., & Wildenberg, J. C. (2023). Analysis of Swin-UNet vision transformer for Inferior Vena Cava filter segmentation from CT scans. Artificial Intelligence in the Life Sciences, 4, 100084.

Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of Recurrent Neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270.

Paparusso, L., Melzi, S., & Braghin, F. (2023). Real-time forecasting of driver-vehicle dynamics on 3D roads: A deep-learning framework leveraging Bayesian optimisation. Transportation Research Part C: Emerging Technologies, 156, 104329.

Yang, X., Yu, S., & Zhou, Y. (2020). LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example. 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), pp. 98-101.

Ghadekar, P., Malwatkar, N., Sontakke N., & Soni, N. (2023). Comparative Analysis of LSTM, GRU and Transformer Models for German to English Language Translation. 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, pp. 1-7.

Wensheng, L., Kuihua, W., Liang, F., Hao, L., Yanshuo, W., & Can, C. (2020). A Region-Level Integrated Energy Load Forecasting Method Based on CNN-LSTM Model with User Energy Label Differentiation. 2020 5th International Conference on Power and Renewable Energy (ICPRE), pp. 154-159.

Abraham, A. V., Sasidharan, P., Tejas, S. J. S., Manohara, M., Muthu, R., & Naidu, R. C. (2022). Predicting Energy Consumption Using LSTM and CNN Deep Learning Algorithm. 2022 7th International Conference on Environment Friendly Energies and Applications (EFEA), Bagatelle Moka MU, pp. 1-6.

Al-Alami, H., & Jamleh, H. O. (2023). Use of Convolutional Neural Networks and Long Short-Term Memory for Accurate Residential Energy Prediction. 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 294-299.

Hamayat, F., Akram, Z., & Zubair, S. (2023). Deep Learning-based Predictive Modeling of Building Energy Usage. 2023 6th International Conference on Energy Conservation and Efficiency (ICECE), pp. 1-6.

Alhussein, M., Aurangzeb, K., & Haider, S. I. (2020). Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting. IEEE Access, 8, pp. 180544-180557.

Shen, H., Wang, Z., Zhou, X., Lamantia, M., Yang, K., Chen, P., & Wang, J. (2022). Electric Vehicle Velocity and Energy Consumption Predictions Using Transformer and Markov-Chain Monte Carlo. IEEE Transactions on Transportation Electrification, vol. 8, no. 3, pp. 3836-3847.

Hu, P., Shan, X., Hu, D., Fu, J., Wang, C., & Zhang, K. (2022). Source-Load Joint Probability Prediction Based on Transformer Model. 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 235-240.

Yuan, J., Chen, S. -Z., Yu, S. S., Zhang, G., Chen, Z., & Zhang, Y. (2023). A Kernel-Based Real-Time Adaptive Dynamic Programming Method for Economic Household Energy Systems. IEEE Transactions on Industrial Informatics, vol. 19, no. 3, pp. 2374-2384.




How to Cite

Kabdygali, S., Omirgaliyev, R., Tursynbayev, T., Kayisli, K. ., & Zhakiyev, N. (2023). DEEP RECURRENT NEURAL NETWORKS IN ENERGY DEMAND FORECASTING: A CASE STUDY OF KAZAKHSTAN’S ELECTRICAL CONSUMPTION. Scientific Journal of Astana IT University, 16(16).