IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2021i1p188-d710915.html
   My bibliography  Save this article

Improved Deep Learning Model Based on Self-Paced Learning for Multiscale Short-Term Electricity Load Forecasting

Author

Listed:
  • Meiping Li

    (Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China)

  • Xiaoming Xie

    (Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China)

  • Du Zhang

    (Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China)

Abstract

Electricity loads are basic and important information for power generation facilities and traders, especially in terms of production plans, daily operations, unit commitments, and economic dispatches. Short-term load forecasting (STLF), which predicts power loads for a few days, plays a vital role in the reliable, safe, and efficient operation of a power system. Currently, two main challenges are faced by existing STLF prediction models. The first involves how to fuse multiscale electricity load data to obtain a high-performance model and remove data noise after integration. The second involves how to improve the local optimal solution despite the sample quality problem. To address the above issues, this paper proposes a multiscale electricity load data fusion- and STLF-based short time series prediction model built on a sparse deep autoencoder and self-paced learning (SPL). A sparse deep autoencoder was used to solve the multiscale data fusion problem with data noise. Furthermore, SPL was utilized to solve the local optimal solution problem. The experimental results showed that our model was better than the existing STLF prediction models by more than 15.89% in terms of the mean squared error (MSE) indicator.

Suggested Citation

  • Meiping Li & Xiaoming Xie & Du Zhang, 2021. "Improved Deep Learning Model Based on Self-Paced Learning for Multiscale Short-Term Electricity Load Forecasting," Sustainability, MDPI, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2021:i:1:p:188-:d:710915
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/1/188/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/1/188/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tao Hong & Jason Wilson & Jingrui Xie, 2013. "Long term probabilistic load forecasting and normalization with hourly information," HSC Research Reports HSC/13/13, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    Full references (including those not matched with items on IDEAS)

    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. Trotter, Ian Michael & Féres, José Gustavo & Bolkesjø, Torjus Folsland & de Hollanda, Lavínia Rocha, 2015. "Simulating Brazilian Electricity Demand Under Climate Change Scenarios," Working Papers in Applied Economics 208689, Universidade Federal de Vicosa, Departamento de Economia Rural.
    2. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics.
    3. Matthias Ritter, 2012. "Can the market forecast the weather better than meteorologists?," SFB 649 Discussion Papers SFB649DP2012-067, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Batalla-Bejerano, Joan & Costa-Campi, Maria Teresa & Trujillo-Baute, Elisa, 2016. "Collateral effects of liberalisation: Metering, losses, load profiles and cost settlement in Spain’s electricity system," Energy Policy, Elsevier, vol. 94(C), pages 421-431.
    5. Miloš Božić & Miloš Stojanović & Zoran Stajić & Dragan Tasić, 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-19, April.
    6. Vaz, Lucélia Viviane & Filho, Getulio Borges da Silveira, 2017. "Functional Autoregressive Models: An Application to Brazilian Hourly Electricity Load," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(2), November.
    7. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    8. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Cohen, Miri Weiss & Reis, Agnaldo J.R. & Silva, Sidelmo M. & Souza, Marcone J.F. & Fleming, Peter J. & Guimarães, Frederico G., 2016. "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, Elsevier, vol. 89(C), pages 730-742.
    9. Niematallah Elamin & Mototsugu Fukushige, 2017. "The 2011 Japanese energy crisis: Effects on the magnitude and pattern of load demand," Discussion Papers in Economics and Business 17-19, Osaka University, Graduate School of Economics.
    10. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
    11. Rubin, Ofir D. & Babcock, Bruce A., 2011. "A novel approach for modeling deregulated electricity markets," Energy Policy, Elsevier, vol. 39(5), pages 2711-2721, May.
    12. Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
    13. repec:qut:auncer:wp103 is not listed on IDEAS
    14. Anand Krishnan Prakash & Susu Xu & Ram Rajagopal & Hae Young Noh, 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models," Energies, MDPI, vol. 11(4), pages 1-21, April.
    15. Lozinskaia, Agata & Redkina, Anastasiia & Shenkman, Evgeniia, 2020. "Electricity consumption forecasting for integrated power system with seasonal patterns," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 60, pages 5-25.
    16. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
    17. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, vol. 29(1), pages 1-12.
    18. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
    19. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    20. Arpita Samanta Santra & Jun-Lin Lin, 2019. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(11), pages 1-11, May.
    21. Ergemen, Yunus Emre & Haldrup, Niels & Rodríguez-Caballero, Carlos Vladimir, 2016. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," Energy Economics, Elsevier, vol. 60(C), pages 79-96.

    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:jsusta:v:14:y:2021:i:1:p:188-:d:710915. 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.