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

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  • 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
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    References listed on IDEAS

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    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).
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