IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p5875-d887392.html
   My bibliography  Save this article

A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition

Author

Listed:
  • Yixiang Ma

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Lean Yu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
    WQ-UCAS Graduate School of Business, Binzhou Institute of Technology, Binzhou 256600, China
    WQ-UCAS Joint Lab, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Guoxing Zhang

    (School of Management, Lanzhou University, Lanzhou 730000, China)

Abstract

To improve the prediction accuracy of short-term load series, this paper proposes a hybrid model based on a multi-trait-driven methodology and secondary decomposition. In detail, four steps were performed sequentially, i.e., data decomposition, secondary decomposition, individual prediction, and ensemble output, all of which were designed based on a multi-trait-driven methodology. In particular, the multi-period identification method and the judgment basis of secondary decomposition were designed to assist the construction of the hybrid model. In the numerical experiment, the short-term load data with 15 min intervals was collected as the research object. By analyzing the results of multi-step-ahead forecasting and the Diebold–Mariano (DM) test, the proposed hybrid model was proven to outperform all benchmark models, which can be regarded as an effective solution for short-term load forecasting.

Suggested Citation

  • Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5875-:d:887392
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/5875/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/16/5875/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohan, Neethu & Soman, K.P. & Sachin Kumar, S., 2018. "A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model," Applied Energy, Elsevier, vol. 232(C), pages 229-244.
    2. Yanbing Lin & Hongyuan Luo & Deyun Wang & Haixiang Guo & Kejun Zhu, 2017. "An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(8), pages 1-16, August.
    3. Emeksiz, Cem & Tan, Mustafa, 2022. "Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach," Energy, Elsevier, vol. 238(PA).
    4. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
    5. Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
    6. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    7. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    8. Guowei Cai & Wenjin Wang & Junhai Lu, 2016. "A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction," Energies, MDPI, vol. 9(12), pages 1-19, November.
    9. Yu, Lean & Ma, Yueming & Ma, Mengyao, 2021. "An effective rolling decomposition-ensemble model for gasoline consumption forecasting," Energy, Elsevier, vol. 222(C).
    10. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    11. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
    12. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
    13. Jiajie Tang & Jie Zhao & Hongliang Zou & Gaoyuan Ma & Jun Wu & Xu Jiang & Huaixun Zhang, 2021. "Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    14. Dai, Yeming & Zhao, Pei, 2020. "A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization," Applied Energy, Elsevier, vol. 279(C).
    15. Wu, Zhuochun & Xia, Xiangjie & Xiao, Liye & Liu, Yilin, 2020. "Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 261(C).
    16. Zhang, Wenyu & Chen, Qian & Yan, Jianyong & Zhang, Shuai & Xu, Jiyuan, 2021. "A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting," Energy, Elsevier, vol. 236(C).
    17. Chahkoutahi, Fatemeh & Khashei, Mehdi, 2017. "A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting," Energy, Elsevier, vol. 140(P1), pages 988-1004.
    18. Hu, Yi & Qu, Boyang & Wang, Jie & Liang, Jing & Wang, Yanli & Yu, Kunjie & Li, Yaxin & Qiao, Kangjia, 2021. "Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning," Applied Energy, Elsevier, vol. 285(C).
    19. Jiang, Ping & Liu, Feng & Song, Yiliao, 2017. "A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting," Energy, Elsevier, vol. 119(C), pages 694-709.
    20. Liu, Xin & Zhang, Zijun & Song, Zhe, 2020. "A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    21. Lopez, J. Humberto, 1997. "The power of the ADF test," Economics Letters, Elsevier, vol. 57(1), pages 5-10, November.
    22. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    23. Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
    24. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
    25. Ling Tang & Lean Yu & Fangtao Liu & Weixuan Xu, 2013. "An Integrated Data Characteristic Testing Scheme For Complex Time Series Data Exploration," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 491-521.
    26. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    27. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
    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. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
    2. Niu, Dongxiao & Ji, Zhengsen & Li, Wanying & Xu, Xiaomin & Liu, Da, 2021. "Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization," Energy, Elsevier, vol. 234(C).
    3. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    4. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    5. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
    6. Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
    7. Hisham Alghamdi & Ghulam Hafeez & Sajjad Ali & Safeer Ullah & Muhammad Iftikhar Khan & Sadia Murawwat & Lyu-Guang Hua, 2023. "An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid," Mathematics, MDPI, vol. 11(21), pages 1-22, November.
    8. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    9. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    10. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh, 2020. "An efficient hour-ahead electrical load forecasting method based on innovative features," Energy, Elsevier, vol. 201(C).
    11. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    12. Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
    13. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    14. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    15. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
    16. Bin Li & Mingzhen Lu & Yiyi Zhang & Jia Huang, 2019. "A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction," Energies, MDPI, vol. 12(20), pages 1-19, October.
    17. 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.
    18. Shichao Huang & Jing Zhang & Yu He & Xiaofan Fu & Luqin Fan & Gang Yao & Yongjun Wen, 2022. "Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer," Energies, MDPI, vol. 15(10), pages 1-14, May.
    19. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
    20. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.

    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:16:p:5875-:d:887392. 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.