IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p8062-d1300114.html
   My bibliography  Save this article

Regional Residential Short-Term Load-Interval Forecasting Based on SSA-LSTM and Load Consumption Consistency Analysis

Author

Listed:
  • Ruixiang Zhang

    (School of Information Science and Technology, Fudan University, Shanghai 200433, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Shanghai 200433, China)

  • Ziyu Zhu

    (School of Information Science and Technology, Fudan University, Shanghai 200433, China
    Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China)

  • Meng Yuan

    (School of Information Science and Technology, Fudan University, Shanghai 200433, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Shanghai 200433, China)

  • Yihan Guo

    (School of Information Science and Technology, Fudan University, Shanghai 200433, China)

  • Jie Song

    (Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Xuanxuan Shi

    (State Grid Nanjing Power Supply Company, Nanjing 210019, China)

  • Yu Wang

    (School of Information Science and Technology, Fudan University, Shanghai 200433, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Shanghai 200433, China)

  • Yaojie Sun

    (School of Information Science and Technology, Fudan University, Shanghai 200433, China
    Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Shanghai 200433, China
    Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China)

Abstract

The electricity consumption behavior of the inhabitants is a major contributor to the uncertainty of the residential load system. Human-caused uncertainty may have a distributional component, but it is not well understood, which limits further understanding the stochastic component of load forecasting. This study proposes a short-term load-interval forecasting method considering the stochastic features caused by users’ electricity consumption behavior. The proposed method is composed of two parts: load-point forecasting using singular spectrum analysis and long short-term memory (SSA-LSTM), and load boundaries forecasting using statistical analysis. Firstly, the load sequence is decomposed and recombined using SSA to obtain regular and stochastic subsequences. Then, the load-point forecasting LSTM network model is trained from the regular subsequence. Subsequently, the load boundaries related to load consumption consistency are forecasted by statistical analysis. Finally, the forecasting results are combined to obtain the load-interval forecasting result. The case study reveals that compared with other common methods, the proposed method can forecast the load interval more accurately and stably based on the load time series. By using the proposed method, the evaluation index coverage rates (CRs) are (17.50%, 1.95%, 1.05%, 0.97%, 7.80%, 4.55%, 9.52%, 1.11%), (17.95%, 3.02%, 1.49%, 5.49%, 5.03%, 1.66%, 1.49%), (19.79%, 2.79%, 1.43%, 1.18%, 3.37%, 1.42%) higher than the compared methods, and the interval average convergences (IACs) are (−18.19%, −8.15%, 3.97%), (36.97%, 21.92%, 22.59%), (12.31%, 21.59%, 7.22%) compared to the existing methods in three different counties, respectively, which shows that the proposed method has better overall performance and applicability through our discussion.

Suggested Citation

  • Ruixiang Zhang & Ziyu Zhu & Meng Yuan & Yihan Guo & Jie Song & Xuanxuan Shi & Yu Wang & Yaojie Sun, 2023. "Regional Residential Short-Term Load-Interval Forecasting Based on SSA-LSTM and Load Consumption Consistency Analysis," Energies, MDPI, vol. 16(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8062-:d:1300114
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/8062/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/24/8062/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Qing & Wang, Hao & Wang, Taotao & Zhang, Shengli & Wu, Xiaoxiao & Wang, Hui, 2021. "Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant," Applied Energy, Elsevier, vol. 294(C).
    2. Serrano-Guerrero, Xavier & Briceño-León, Marco & Clairand, Jean-Michel & Escrivá-Escrivá, Guillermo, 2021. "A new interval prediction methodology for short-term electric load forecasting based on pattern recognition," Applied Energy, Elsevier, vol. 297(C).
    3. Oreshkin, Boris N. & Dudek, Grzegorz & Pełka, Paweł & Turkina, Ekaterina, 2021. "N-BEATS neural network for mid-term electricity load forecasting," Applied Energy, Elsevier, vol. 293(C).
    4. Sirignano, Justin & Spiliopoulos, Konstantinos, 2020. "Mean field analysis of neural networks: A central limit theorem," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1820-1852.
    5. Yizhen Wang & Ningqing Zhang & Xiong Chen, 2021. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features," Energies, MDPI, vol. 14(10), pages 1-13, May.
    6. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
    7. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    8. 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).
    9. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    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. 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).
    2. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    3. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    4. Feng Gao & Jie Song & Xueyan Shao, 2025. "Short-term interval-valued load forecasting with a combined strategy of iHW and multioutput machine learning," Annals of Operations Research, Springer, vol. 346(3), pages 2009-2033, March.
    5. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    6. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
    7. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org, revised Feb 2025.
    8. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    9. Rui Luo & Jinpei Liu & Piao Wang & Zhifu Tao & Huayou Chen, 2024. "A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 366-390, March.
    10. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George J. Anders, 2024. "A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets," Energies, MDPI, vol. 17(10), pages 1-28, May.
    11. Niraj Buyo & Akbar Sheikh-Akbari & Farrukh Saleem, 2025. "An Ensemble Approach to Predict a Sustainable Energy Plan for London Households," Sustainability, MDPI, vol. 17(2), pages 1-30, January.
    12. 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.
    13. Jinpeng Yang, 2023. "Transaction decision optimization of new electricity market based on virtual power plant participation and Stackelberg game," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-20, April.
    14. Sebastian Jaimungal, 2022. "Reinforcement learning and stochastic optimisation," Finance and Stochastics, Springer, vol. 26(1), pages 103-129, January.
    15. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    16. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    17. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    18. He, Yan & Zhang, Hongli & Dong, Yingchao & Wang, Cong & Ma, Ping, 2024. "Residential net load interval prediction based on stacking ensemble learning," Energy, Elsevier, vol. 296(C).
    19. Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.
    20. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).

    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:16:y:2023:i:24:p:8062-:d:1300114. 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.