IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i22p6154-d449826.html
   My bibliography  Save this article

Deep Learning Approach to Power Demand Forecasting in Polish Power System

Author

Listed:
  • Tomasz Ciechulski

    (Faculty of Electronics, Military University of Technology, ul. gen. S. Kaliskiego 2, 00-908 Warsaw, Poland)

  • Stanisław Osowski

    (Faculty of Electronics, Military University of Technology, ul. gen. S. Kaliskiego 2, 00-908 Warsaw, Poland
    Faculty of Electrical Engineering, Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland)

Abstract

The paper presents a new approach to predicting the 24-h electricity power demand in the Polish Power System (PPS, or Krajowy System Elektroenergetyczny—KSE) using the deep learning approach. The prediction system uses a deep multilayer autoencoder to generate diagnostic features and an ensemble of two neural networks: multilayer perceptron and radial basis function network and support vector machine in regression model, for final 24-h forecast one-week advance. The period of the data that is the subject of the experiments is 2014–2019, which has been divided into two parts: Learning data (2014–2018), and test data (2019). The numerical experiments have shown the advantage of deep learning over classical approaches of neural networks for the problem of power demand prediction.

Suggested Citation

  • Tomasz Ciechulski & Stanisław Osowski, 2020. "Deep Learning Approach to Power Demand Forecasting in Polish Power System," Energies, MDPI, vol. 13(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6154-:d:449826
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/22/6154/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/22/6154/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    2. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
    3. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    4. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    5. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
    6. Khoshrou, Abdolrahman & Pauwels, Eric J., 2019. "Short-term scenario-based probabilistic load forecasting: A data-driven approach," Applied Energy, Elsevier, vol. 238(C), pages 1258-1268.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Robert Basmadjian & Amirhossein Shaafieyoun & Sahib Julka, 2021. "Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods," Energies, MDPI, vol. 14(21), pages 1-23, November.
    2. Stanislaw Osowski & Robert Szmurlo & Krzysztof Siwek & Tomasz Ciechulski, 2022. "Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study," Energies, MDPI, vol. 15(9), pages 1-21, April.
    3. Michal Pavlicko & Mária Vojteková & Oľga Blažeková, 2022. "Forecasting of Electrical Energy Consumption in Slovakia," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    4. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    5. Tomasz Ciechulski & Stanisław Osowski, 2021. "High Precision LSTM Model for Short-Time Load Forecasting in Power Systems," Energies, MDPI, vol. 14(11), pages 1-15, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stanislaw Osowski & Robert Szmurlo & Krzysztof Siwek & Tomasz Ciechulski, 2022. "Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study," Energies, MDPI, vol. 15(9), pages 1-21, April.
    2. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
    3. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
    4. Yi Yang & Zhihao Shang & Yao Chen & Yanhua Chen, 2020. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting," Energies, MDPI, vol. 13(3), pages 1-19, January.
    5. Yuhong Xie & Yuzuru Ueda & Masakazu Sugiyama, 2021. "A Two-Stage Short-Term Load Forecasting Method Using Long Short-Term Memory and Multilayer Perceptron," Energies, MDPI, vol. 14(18), pages 1-17, September.
    6. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    7. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    8. Liu Lu & Wei Wei, 2023. "Influence of Public Sports Services on Residents’ Mental Health at Communities Level: New Insights from China," IJERPH, MDPI, vol. 20(2), pages 1-14, January.
    9. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski & Ibrahim Alhamrouni, 2024. "AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review," Sustainability, MDPI, vol. 16(12), pages 1-35, June.
    10. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    11. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    12. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    13. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    14. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    15. Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 681-701, May.
    16. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    17. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    18. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    19. Kanitta Yarak & Apichon Witayangkurn & Kunnaree Kritiyutanont & Chomchanok Arunplod & Ryosuke Shibasaki, 2021. "Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
    20. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6154-:d:449826. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.