IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v4y2022i1p8-164d732285.html
   My bibliography  Save this article

Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Author

Listed:
  • Venkataramana Veeramsetty

    (Center for Artificial Intelligence and Deep Learning, Department of Electrical and Electronics Engineering, SR University, Warangal 506371, India)

  • Dongari Rakesh Chandra

    (Department of Electrical and Electronics Engineering, Kakatiya Institute of Technology and Science (KITS), Warangal 506015, India)

  • Francesco Grimaccia

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Marco Mussetta

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

Abstract

Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning and data analytics models represent a valuable tool to cope with the intrinsic complexity and especially design future demand-side advanced services. The main novelty in this paper is that the combination of a Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques is proposed to improve the forecasting capability of the hourly load on an electric power substation. A historical dataset of measured loads related to a 33/11 kV MV substation is considered in India as a case study, in order to properly validate the designed method. Based on the presented numerical results, the proposed approach proved itself to accurately predict loads with a reduced dimensionality of input data, thus minimizing the overall computational effort.

Suggested Citation

  • Venkataramana Veeramsetty & Dongari Rakesh Chandra & Francesco Grimaccia & Marco Mussetta, 2022. "Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks," Forecasting, MDPI, vol. 4(1), pages 1-16, January.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:1:p:8-164:d:732285
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/4/1/8/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/4/1/8/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pengwei Su & Xue Tian & Yan Wang & Shuai Deng & Jun Zhao & Qingsong An & Yongzhen Wang, 2017. "Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System," Energies, MDPI, vol. 10(9), pages 1-13, August.
    2. Mansoor, Muhammad & Grimaccia, Francesco & Leva, Sonia & Mussetta, Marco, 2021. "Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 282-293.
    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. Carla Sahori Seefoo Jarquin & Alessandro Gandelli & Francesco Grimaccia & Marco Mussetta, 2023. "Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks," Forecasting, MDPI, vol. 5(2), pages 1-15, April.
    2. Morais, Lucas Barros Scianni & Aquila, Giancarlo & de Faria, Victor Augusto Durães & Lima, Luana Medeiros Marangon & Lima, José Wanderley Marangon & de Queiroz, Anderson Rodrigo, 2023. "Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system," Applied Energy, Elsevier, vol. 348(C).
    3. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos, 2022. "ReNFuzz-LF: A Recurrent Neurofuzzy System for Short-Term Load Forecasting," Energies, MDPI, vol. 15(10), pages 1-18, May.
    4. Mahdi Khodayar & Jacob Regan, 2023. "Deep Neural Networks in Power Systems: A Review," Energies, MDPI, vol. 16(12), pages 1-38, June.
    5. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos & Constantinos Hilas, 2023. "Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network," Energies, MDPI, vol. 16(10), pages 1-20, 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. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    2. Simos, Theodore E. & Katsikis, Vasilios N. & Mourtas, Spyridon D., 2022. "Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 451-465.
    3. Ayman Esmat & Julio Usaola & Mª Ángeles Moreno, 2018. "A Decentralized Local Flexibility Market Considering the Uncertainty of Demand," Energies, MDPI, vol. 11(8), pages 1-32, August.
    4. Pedro M. R. Bento & Jose A. N. Pombo & Maria R. A. Calado & Silvio J. P. S. Mariano, 2021. "Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting," Energies, MDPI, vol. 14(21), pages 1-21, November.
    5. Zhigang Duan & Yamin Yan & Xiaohan Yan & Qi Liao & Wan Zhang & Yongtu Liang & Tianqi Xia, 2017. "An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand," Energies, MDPI, vol. 11(1), pages 1-23, December.
    6. Vidya Krishnan Mololoth & Saguna Saguna & Christer Åhlund, 2023. "Blockchain and Machine Learning for Future Smart Grids: A Review," Energies, MDPI, vol. 16(1), pages 1-39, January.
    7. Dietrich, Bastian & Walther, Jessica & Weigold, Matthias & Abele, Eberhard, 2020. "Machine learning based very short term load forecasting of machine tools," Applied Energy, Elsevier, vol. 276(C).
    8. Delagnes, T. & Henneron, T. & Clenet, S. & Fratila, M. & Ducreux, J.P., 2023. "Comparison of reduced basis construction methods for Model Order Reduction, with application to non-linear low frequency electromagnetics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 211(C), pages 470-488.
    9. Carla Sahori Seefoo Jarquin & Alessandro Gandelli & Francesco Grimaccia & Marco Mussetta, 2023. "Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks," Forecasting, MDPI, vol. 5(2), pages 1-15, April.
    10. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    11. 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.

    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:jforec:v:4:y:2022:i:1:p:8-164:d:732285. 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.