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
DOI:
https://doi.org/10.15587/1729-4061.2024.299192Keywords:
short-term load forecasting, recurrent neural networks, long short-term memory-based load forecastingAbstract
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
References
- Ilyasov, A. Z., Saukhimov, A. A., Didorenko, Y. V., Landman, A. K., Janzakov, Y. B. (2022). Creation of a automation generation control system in the unified power system of Kazakhstan. Bulletin of the Almaty University of Power Engineering and Telecommunications, 1, 6–17. https://doi.org/10.51775/2790-0886_2022_56_1_6
- O rabote balansiruyushchego rynka elektricheskoy energii. Kazahstanskaya kompaniya po upravleniyu elektricheskimi setyami. Available at: https://www.kegoc.kz/ru/electric-power/etn/
- Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (2008). Time Series Analysis. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc. https://doi.org/10.1002/9781118619193
- Tarmanini, C., Sarma, N., Gezegin, C., Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550–557. https://doi.org/10.1016/j.egyr.2023.01.060
- Huang, S.-J., Shih, K.-R. (2003). Short-term load forecasting via ARMA model identification including non-gaussian process considerations. IEEE Transactions on Power Systems, 18 (2), 673–679. https://doi.org/10.1109/tpwrs.2003.811010
- Sayed, H. A., William, A., Said, A. M. (2023). Smart Electricity Meter Load Prediction in Dubai Using MLR, ANN, RF, and ARIMA. Electronics, 12 (2), 389. https://doi.org/10.3390/electronics12020389
- Moghram, I., Rahman, S. (1989). Analysis and evaluation of five short-term load forecasting techniques. IEEE Transactions on Power Systems, 4 (4), 1484–1491. https://doi.org/10.1109/59.41700
- Barakat, E. H., Qayyum, M. A., Hamed, M. N., Al Rashed, S. A. (1990). Short-term peak demand forecasting in fast developing utility with inherit dynamic load characteristics. I. Application of classical time-series methods. II. Improved modelling of system dynamic load characteristics. IEEE Transactions on Power Systems, 5 (3), 813–824. https://doi.org/10.1109/59.65910
- Wazirali, R., Yaghoubi, E., Abujazar, M. S. S., Ahmad, R., Vakili, A. H. (2023). State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques. Electric Power Systems Research, 225, 109792. https://doi.org/10.1016/j.epsr.2023.109792
- Cai, W., Wen, X., Li, C., Shao, J., Xu, J. (2023). Predicting the energy consumption in buildings using the optimized support vector regression model. Energy, 273, 127188. https://doi.org/10.1016/j.energy.2023.127188
- Yusuf, L. O. M., Handoko, S., Setiawan, I. (2023). Forecasting Short-Term Electrical Loads Using Support Vector Regression with Gaussian Kernel Functions. Advances in Physics Research, 131–142. https://doi.org/10.2991/978-94-6463-228-6_16
- Choi, W., Lee, S. (2023). Performance evaluation of deep learning architectures for load and temperature forecasting under dataset size constraints and seasonality. Energy and Buildings, 288, 113027. https://doi.org/10.1016/j.enbuild.2023.113027
- Aseeri, A. O. (2023). Effective RNN-Based Forecasting Methodology Design for Improving Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series. Journal of Computational Science, 68, 101984. https://doi.org/10.1016/j.jocs.2023.101984
- Jayashankara, M., Shah, P., Sharma, A., Chanak, P., Singh, S. K. (2023). A Novel Approach for Short-Term Energy Forecasting in Smart Buildings. IEEE Sensors Journal, 23 (5), 5307–5314. https://doi.org/10.1109/jsen.2023.3237876
- Singh, S., Tripathi, M. M. (2022). Short-Term Electricity Demand Forecast Using Deep RNN and Stacked LSTM. Advances in Manufacturing Technology and Management, 578–588. https://doi.org/10.1007/978-981-16-9523-0_64
- Haque, A., Rahman, S. (2022). Short-term electrical load forecasting through heuristic configuration of regularized deep neural network. Applied Soft Computing, 122, 108877. https://doi.org/10.1016/j.asoc.2022.108877
- Alhmoud, L., Abu Khurma, R., Al-Zoubi, A. M., Aljarah, I. (2021). A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network. Sensors, 21 (18), 6240. https://doi.org/10.3390/s21186240
- Meng, Y., Yun, S., Zhao, Z., Guo, J., Li, X., Ye, D. et al. (2023). Short-term electricity load forecasting based on a novel data preprocessing system and data reconstruction strategy. Journal of Building Engineering, 77, 107432. https://doi.org/10.1016/j.jobe.2023.107432
- Jalali, S. M. J., Ahmadian, S., Khosravi, A., Shafie-khah, M., Nahavandi, S., Catalao, J. P. S. (2021). A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting. IEEE Transactions on Industrial Informatics, 17 (12), 8243–8253. https://doi.org/10.1109/tii.2021.3065718
- Yin, L., Xie, J. (2021). Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems. Applied Energy, 283, 116328. https://doi.org/10.1016/j.apenergy.2020.116328
- Tang, X., Chen, H., Xiang, W., Yang, J., Zou, M. (2022). Short-Term Load Forecasting Using Channel and Temporal Attention Based Temporal Convolutional Network. Electric Power Systems Research, 205, 107761. https://doi.org/10.1016/j.epsr.2021.107761
- Hochreiter, S., Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9 (8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Hochreiter, S, Schmidhuber, J. (1996). LSTM can solve hard long time lag problems. In Advances in neural information processing systems. Vol. 9. MIT Press, 473–479. Available at: https://proceedings.neurips.cc/paper_files/paper/1996/file/a4d2f0d23dcc84ce983ff9157f8b7f88-Paper.pdf
- Kingma, D. P., Ba, J. (2014). Adam: A method for stochastic optimization. arXiv. https://doi.org/10.48550/arXiv.1412.6980
- Zou, F., Shen, L., Jie, Z., Zhang, W., Liu, W. (2019). A Sufficient Condition for Convergences of Adam and RMSProp. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.01138
- Caicedo-Vivas, J. S., Alfonso-Morales, W. (2023). Short-Term Load Forecasting Using an LSTM Neural Network for a Grid Operator. Energies, 16 (23), 7878. https://doi.org/10.3390/en16237878
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Yerlan Kenessov, Karmel Tokhtibakiev, Almaz Saukhimov, Daniil Vassilyev, Alexandr Gunin, Azamat Iliyasov
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
