IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4198-d833496.html
   My bibliography  Save this article

Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management

Author

Listed:
  • Fan Yu

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

  • Lei Wang

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China
    Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an 710048, China)

  • Qiaoyong Jiang

    (Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an 710048, China)

  • Qunmin Yan

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

  • Shi Qiao

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

Abstract

Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to optimize the model parameters. Cross-sectional and longitudinal experiments are conducted on a regional-level load dataset set in Spain. The experimental results prove that the proposed method is superior to other methods in STLF.

Suggested Citation

  • Fan Yu & Lei Wang & Qiaoyong Jiang & Qunmin Yan & Shi Qiao, 2022. "Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management," Energies, MDPI, vol. 15(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4198-:d:833496
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4198/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/12/4198/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    2. Andrés M. Alonso & Francisco J. Nogales & Carlos Ruiz, 2020. "A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series," Energies, MDPI, vol. 13(20), pages 1-19, October.
    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. Fatma Yaprakdal, 2022. "An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods," Energies, MDPI, vol. 16(1), pages 1-13, December.

    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. 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.
    2. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    3. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    4. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    5. 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.
    6. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    7. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    8. Brucke, Karoline & Arens, Stefan & Telle, Jan-Simon & Steens, Thomas & Hanke, Benedikt & von Maydell, Karsten & Agert, Carsten, 2021. "A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings," Applied Energy, Elsevier, vol. 292(C).
    9. Sulaiman, Mohd Herwan & Mustaffa, Zuriani, 2024. "Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach," Energy, Elsevier, vol. 297(C).
    10. Ma, Tao & Zhang, Yijie & Gu, Wenbo & Xiao, Gang & Yang, Hongxing & Wang, Shuxiao, 2022. "Strategy comparison and techno-economic evaluation of a grid-connected photovoltaic-battery system," Renewable Energy, Elsevier, vol. 197(C), pages 1049-1060.
    11. Gi-Wook Cha & Won-Hwa Hong & Young-Chan Kim, 2023. "Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    12. Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.
    13. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    14. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
    15. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    16. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    17. Wang, Haoxuan & Chen, Huaian & Wang, Ben & Jin, Yi & Li, Guiqiang & Kan, Yan, 2022. "High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network," Applied Energy, Elsevier, vol. 318(C).
    18. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    19. Cameron Francis Assadian & Francis Assadian, 2023. "Data-Driven Modeling of Appliance Energy Usage," Energies, MDPI, vol. 16(22), pages 1-12, November.
    20. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.

    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:12:p:4198-:d:833496. 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.