IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3659-d817187.html
   My bibliography  Save this article

Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer

Author

Listed:
  • Shichao Huang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Jing Zhang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Yu He

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Xiaofan Fu

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Luqin Fan

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Gang Yao

    (Guizhou Power Grid Company, Guiyang 550001, China)

  • Yongjun Wen

    (Pujiang Guangyuan Power Construction Co., Ltd., Pujiang, Jinhua 322200, China)

Abstract

Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Transformer model was 1.1317%, while the RMSE was 304.40, which was better than the selected six forecasting models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3659-:d:817187
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3659/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/10/3659/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    2. 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.
    3. 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.
    4. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    5. Xin Gao & Xiaobing Li & Bing Zhao & Weijia Ji & Xiao Jing & Yang He, 2019. "Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection," Energies, MDPI, vol. 12(6), pages 1-18, March.
    6. Fan, Guo-Feng & Yu, Meng & Dong, Song-Qiao & Yeh, Yi-Hsuan & Hong, Wei-Chiang, 2021. "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling," Utilities Policy, Elsevier, vol. 73(C).
    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.
    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. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    2. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
    3. Shi, Jian & Teh, Jiashen, 2024. "Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion," Applied Energy, Elsevier, vol. 353(PB).

    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. Mingping Liu & Xihao Sun & Qingnian Wang & Suhui Deng, 2022. "Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model," Energies, MDPI, vol. 15(19), pages 1-22, September.
    2. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    3. 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).
    4. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
    5. Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    6. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    7. Qingqing Ji & Shiyu Zhang & Qiao Duan & Yuhan Gong & Yaowei Li & Xintong Xie & Jikang Bai & Chunli Huang & Xu Zhao, 2022. "Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion," Mathematics, MDPI, vol. 10(12), pages 1-30, June.
    8. 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.
    9. Frederick Nsambu Kijjambu & Benjamin Musiita & Asaph Kaburura Katarangi & Geoffrey Kahangane & Sheilla Akampwera, 2023. "Determinants of Uganda’s Debt Sustainability: The Public Debt Dynamics Model in Perspective," Journal of Economics and Behavioral Studies, AMH International, vol. 15(4), pages 106-124.
    10. Xiaoyu Gao & Chengying Qi & Guixiang Xue & Jiancai Song & Yahui Zhang & Shi-ang Yu, 2020. "Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR," Energies, MDPI, vol. 13(22), pages 1-19, November.
    11. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    12. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    13. Dong, Ming & Shi, Jian & Shi, Qingxin, 2020. "Multi-year long-term load forecast for area distribution feeders based on selective sequence learning," Energy, Elsevier, vol. 206(C).
    14. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    15. Dong, Hanjiang & Zhu, Jizhong & Li, Shenglin & Wu, Wanli & Zhu, Haohao & Fan, Junwei, 2023. "Short-term residential household reactive power forecasting considering active power demand via deep Transformer sequence-to-sequence networks," Applied Energy, Elsevier, vol. 329(C).
    16. 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.
    17. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    18. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    19. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    20. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.

    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:10:p:3659-:d:817187. 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.