IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v256y2022ics0360544222016255.html
   My bibliography  Save this article

Short-term load forecasting using detrend singular spectrum fluctuation analysis

Author

Listed:
  • Wei, Nan
  • Yin, Lihua
  • Li, Chao
  • Wang, Wei
  • Qiao, Weibiao
  • Li, Changjun
  • Zeng, Fanhua
  • Fu, Lingdi

Abstract

The accuracy of short-term load forecasting (STLF) is susceptible to the complex components of original time series. Conventional data decomposition algorithms, such as singular spectrum analysis (SSA), cannot determine the complex components and thus fails to improve the accuracy of STLF significantly. Given this, this paper proposes a novel decomposition algorithm, namely detrend singular spectrum fluctuation analysis (DSSFA), to improve the accuracy of STLF. The novel algorithm extracts the trend and periodic components from original series using linear and sine function, respectively. The rest series are decomposed by SSA and the long-rang correlation components without white noise are obtained using fluctuation analysis. Long short-term memory (LSTM) is the model used for forecasting the long-rang correlation components. Combining the forecasts of trend, periodic, and long-rang correlation components, we receive the final forecasting results of DSSFA-LSTM. In our experiments, we design a case study with the most recent load of four exit points within natural gas pipeline. The results show that DSSFA outperforms SSA in improving the performance of forecasting models, when dealing with the short-term load series with high complexity. In Oinofyta, DSSFA-LSTM perfectly fit the real load series and its R2 is 1.8 times higher than that of LSTM.

Suggested Citation

  • Wei, Nan & Yin, Lihua & Li, Chao & Wang, Wei & Qiao, Weibiao & Li, Changjun & Zeng, Fanhua & Fu, Lingdi, 2022. "Short-term load forecasting using detrend singular spectrum fluctuation analysis," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222016255
    DOI: 10.1016/j.energy.2022.124722
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222016255
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124722?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Moreno, Sinvaldo Rodrigues & dos Santos Coelho, Leandro, 2018. "Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System," Renewable Energy, Elsevier, vol. 126(C), pages 736-754.
    3. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    4. Afshar, K. & Bigdeli, N., 2011. "Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)," Energy, Elsevier, vol. 36(5), pages 2620-2627.
    5. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
    6. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    7. 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).
    8. Imani, Maryam & Ghassemian, Hassan, 2019. "Residential load forecasting using wavelet and collaborative representation transforms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    10. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    11. Tayab, Usman Bashir & Zia, Ali & Yang, Fuwen & Lu, Junwei & Kashif, Muhammad, 2020. "Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform," Energy, Elsevier, vol. 203(C).
    12. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    13. Wei, Nan & Yin, Lihua & Li, Chao & Li, Changjun & Chan, Christine & Zeng, Fanhua, 2021. "Forecasting the daily natural gas consumption with an accurate white-box model," Energy, Elsevier, vol. 232(C).
    14. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    15. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
    16. Nan Wei & Changjun Li & Jiehao Duan & Jinyuan Liu & Fanhua Zeng, 2019. "Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model," Energies, MDPI, vol. 12(2), pages 1-15, January.
    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. Prajowal Manandhar & Hasan Rafiq & Edwin Rodriguez-Ubinas & Themis Palpanas, 2024. "New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting," Energies, MDPI, vol. 17(23), pages 1-30, December.
    2. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    3. Chuang Yin & Nan Wei & Jinghang Wu & Chuhong Ruan & Xi Luo & Fanhua Zeng, 2024. "An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting," Energies, MDPI, vol. 17(2), pages 1-17, January.
    4. Wei, Nan & Yin, Chuang & Yin, Lihua & Tan, Jingyi & Liu, Jinyuan & Wang, Shouxi & Qiao, Weibiao & Zeng, Fanhua, 2024. "Short-term load forecasting based on WM algorithm and transfer learning model," Applied Energy, Elsevier, vol. 353(PA).
    5. Liu, Jinyuan & Wang, Shouxi & Wei, Nan & Qiao, Weibiao & Li, Ze & Zeng, Fanhua, 2023. "A clustering-based feature enhancement method for short-term natural gas consumption forecasting," Energy, Elsevier, vol. 278(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. Liu, Jinyuan & Wang, Shouxi & Wei, Nan & Qiao, Weibiao & Li, Ze & Zeng, Fanhua, 2023. "A clustering-based feature enhancement method for short-term natural gas consumption forecasting," Energy, Elsevier, vol. 278(PB).
    2. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    3. Fang, Yu & Jia, Chunhong & Wang, Xin & Min, Fan, 2024. "A fusion gas load prediction model with three-way residual error amendment," Energy, Elsevier, vol. 294(C).
    4. Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
    5. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
    6. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    7. Alexandros Menelaos Tzortzis & Sotiris Pelekis & Evangelos Spiliotis & Evangelos Karakolis & Spiros Mouzakitis & John Psarras & Dimitris Askounis, 2023. "Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series," Mathematics, MDPI, vol. 12(1), pages 1-24, December.
    8. Rundong Gong & Xiukun Wang & Lei Li & Kaikai Li & Ran An & Chenggang Xian, 2022. "Lattice Boltzmann Modeling of Spontaneous Imbibition in Variable-Diameter Capillaries," Energies, MDPI, vol. 15(12), pages 1-19, June.
    9. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    10. Wei, Nan & Yin, Chuang & Yin, Lihua & Tan, Jingyi & Liu, Jinyuan & Wang, Shouxi & Qiao, Weibiao & Zeng, Fanhua, 2024. "Short-term load forecasting based on WM algorithm and transfer learning model," Applied Energy, Elsevier, vol. 353(PA).
    11. Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, vol. 10(7), pages 1-29, July.
    12. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    13. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
    14. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    15. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    16. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
    17. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    18. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    19. Qiang Shang & Ciyun Lin & Zhaosheng Yang & Qichun Bing & Xiyang Zhou, 2016. "A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
    20. Emeksiz, Cem & Tan, Mustafa, 2022. "Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)," Energy, Elsevier, vol. 249(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:eee:energy:v:256:y:2022:i:c:s0360544222016255. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.