Forecasting of short-term power demands in Polish Power System using ensemble of LSTM networks

• Autorzy: Ciechulski Tomasz

Identyfikatory

DOI

10.24136/jaeee.2025.003

• Języki publikacji: EN

Abstrakty

EN

The article presents and discusses the results of the research of forecasting power demands in Polish Power System with time horizon of one hour ahead in conditions of limited availability of forecasting model input data, covering only three months. The prediction was carried out using deep neural networks - LSTM (Long Short-Term Memory) connected to an ensemble. The performance of the ensemble is much more efficient than individual networks working separately. The numerical experiments were conducted using MATLAB computing environment. The accuracy of the predictions was estimated using such statistical measures as MAPE, MAE, RMSE, Pearson correlation coefficient R.

Słowa kluczowe

EN

LSTM; neural networks; Polish Power System; power demand; time series forecasting

• Wydawca: Uniwersytet Radomski im. Kazimierza Pułaskiego
• Czasopismo: Journal of Automation, Electronics and Electrical Engineering
• Rocznik: 2025
• Tom: Vol. 7, No. 1
• Strony: 29--37
• Opis fizyczny: Bibliogr. 20 poz., rys., wykr.

Twórcy

autor

Ciechulski Tomasz

• Military University of Technology, Faculty of Electronics, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

Bibliografia

• [1] Herui C., Xu P., Yupei M., “Electric Load Forecast Using Combined Models with HP Filter-SARIMA and ARMAX Optimized by Regression Analysis Algorithm”, Mathematical Problems in Engineering, 2015, 5, pp. 1–14, https://doi.org/10.1155/2015/386925
• [2] Kuo P.-H., Huang C.-J., “A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting”, Energies 2018, 11, 213, https://doi.org/10.3390/en11010213
• [3] Ceperic E., Ceperic V., Baric A., “A strategy for short-term load forecasting by Support Vector Regression Machines”, IEEE Transactions on Power Systems, 2013, 11, 4356-4364, https://doi.org/10.1109/TPWRS.2013.2269803
• [4] Moon J., Kim Y., Son M., Hwang E., “Hybrid short-term load forecasting scheme using random forest and multilayer perceptron”, Energies, 2018, 11, 3283, 1:20, https://doi.org/10.3390/en11123283
• [5] Yang Y., Chen Y., Wang Y., Li C., Li L., “Modelling a combined method based on ANFIS and neural network improved by DE algorithm: a case study for short-term electricity demand forecasting”, Applied Soft Computing, 2016, 49, 663–675, https://doi.org/10.1016/j.asoc.2016.07.053
• [6] He W., “Load forecasting via deep neural networks”, Procedia Computer Science, 2017, 122, 308-314, https://doi.org/10.1016/j.procs.2017.11.374
• [7] Ciechulski T., Osowski S., “High Precision LSTM Model for Short-Time Load Forecasting in Power Systems”, Energies, 2021, 14, 2983, https://doi.org/10.3390/en14112983
• [8] Hochreiter S., Schmidhuber J., “Long short-term memory”, Neural Computation, 1997, 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735
• [9] Schmidhuber J., “Deep learning in neural networks: An overview”, Neural Networks, 2015, 61, 85–117, https://doi.org/10.1016/j.neunet.2014.09.003
• [10] Greff K., Srivastava R.K., Koutnik J., Steunebrink B.R., Schmidhuber J., “LSTM: A Search Space Odyssey”, IEEE Transactions on Neural Networks and Learning Systems, 2017, 28, 2222–2232, https://doi.org/10.1109/TNNLS.2016.2582924
• [11] Kumar J., Goomer R., Singh A.K., “Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters”, Procedia Computer Sciences, 2018, 125, 676–682, https://doi.org/10.1016/j.procs.2017.12.087
• [12] Osowski S., Szmurło R., Siwek K., Ciechulski T., „Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study”, Energies, 2022, 15, 3265, https://doi.org/10.3390/en15093265
• [13] Polish Power System reports, available online: https://www.pse.pl/obszary-dzialalnosci/krajowy-system-elektroenergetyczny/zapotrzebowanie-kse (accessed on 15 December 2024)
• [14] MathWorks, “MATLAB manual user’s guide”, Natick, 2023
• [15] Jiménez J.M., Stokes L., Moss C., Yang Q., Livina V.N., “Modelling energy demand response using long short-term memory neural networks”, Energy Efficiency, 2020, 13, 1263–1280, https://doi.org/10.1007/s12053-020-09879-z
• [16] Wang H., Li M., Yue X., “IncLSTM: Incremental Ensemble LSTM Model towards Time Series Data”, Comput. Electr. Eng., 2021, 92, https://doi.org/10.1016/j.compeleceng.2021.107156
• [17] Yamasaki Junior M., Freire R.Z., Seman L.O., Stefenon S.F., Mariani V.C., Coelho L.S., "Optimized hybrid ensemble learning approaches applied to very short-term load forecasting", International Journal of Electrical Power & Energy Systems, volume 155, part B, January 2024, 109579, https://doi.org/10.1016/j.ijepes.2023.109579
• [18] Phyo P.-P., Jeenanunta C., "Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering", Applied Sciences, 2022, 12, 4882, https://doi.org/10.3390/app12104882
• [19] Rodríguez F., Maqueda E., Fernández M., Pimenta P., Marques M.I., "A novel methodology for day-ahead buildings energy demand forecasting to provide flexibility services in energy markets", International Journal of Electrical Power & Energy Systems, volume 161, 2024, 110207, https://doi.org/10.1016/j.ijepes.2024.110207
• [20] Duan Y., "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning", Sustainability, 2022, 14, 8584, https://doi.org/10.3390/su14148584