IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5375-d871030.html
   My bibliography  Save this article

Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy

Author

Listed:
  • Fangze Zhou

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Hui Zhou

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Zhaoyan Li

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Kai Zhao

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The electricity load forecasting plays a pivotal role in the operation of power utility companies precise forecasting and is crucial to mitigate the challenges of supply and demand in the smart grid. More recently, the hybrid models combining signal decomposition and artificial neural networks have received popularity due to their applicability to reduce the difficulty of prediction. However, the commonly used decomposition algorithms and recurrent neural network-based models still confront some dilemmas such as boundary effects, time consumption, etc. Therefore, a hybrid prediction model combining variational mode decomposition (VMD), a temporal convolutional network (TCN), and an error correction strategy is proposed. To address the difficulty in determining the decomposition number and penalty factor for VMD decomposition, the idea of weighted permutation entropy is introduced. The decomposition hyperparameters are optimized by using a comprehensive indicator that takes account of the complexity and amplitude of the subsequences. Besides, a temporal convolutional network is adopted to carry out feature extraction and load prediction for each subsequence, with the primary forecasting results obtained by combining the prediction of each TCN model. In order to further improve the accuracy of prediction for the model, an error correction strategy is applied according to the prediction error of the train set. The Global Energy Competition 2014 dataset is employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction performance of the proposed hybrid model outperforms the contrast models. The accuracy achieves 0.274%, 0.326%, and 0.405 for 6-steps, 12-steps, and 24 steps ahead forecasting, respectively, in terms of the mean absolute percentage error.

Suggested Citation

  • Fangze Zhou & Hui Zhou & Zhaoyan Li & Kai Zhao, 2022. "Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy," Energies, MDPI, vol. 15(15), pages 1-18, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5375-:d:871030
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5375/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/15/5375/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grzegorz Marcjasz & Jesus Lago & Rafa{l} Weron, 2020. "Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs," Papers 2008.08006, arXiv.org.
    2. Tarsitano, Agostino & Amerise, Ilaria L., 2017. "Short-term load forecasting using a two-stage sarimax model," Energy, Elsevier, vol. 133(C), pages 108-114.
    3. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    4. Jianwei Mi & Libin Fan & Xuechao Duan & Yuanying Qiu, 2018. "Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, March.
    5. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    6. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    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. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    2. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    3. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-24, February.

    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. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    2. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
    3. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang, 2020. "Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting," Applied Energy, Elsevier, vol. 270(C).
    4. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    5. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    6. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    7. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    8. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    9. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    10. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    11. Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    12. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    13. Taorong Jia & Lixiao Yao & Guoqing Yang & Qi He, 2022. "A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    14. Qiangqiang Cheng & Yiqi Yan & Shichao Liu & Chunsheng Yang & Hicham Chaoui & Mohamad Alzayed, 2020. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling," Energies, MDPI, vol. 13(24), pages 1-15, December.
    15. Aida Boudhaouia & Patrice Wira, 2021. "A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning," Forecasting, MDPI, vol. 3(4), pages 1-13, September.
    16. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    17. Jieyi Kang & David Reiner, 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers EPRG2113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    18. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    19. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    20. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.

    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:15:y:2022:i:15:p:5375-:d:871030. 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.