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Residential load forecasting using wavelet and collaborative representation transforms

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  • Imani, Maryam
  • Ghassemian, Hassan

Abstract

Short-term household-level load forecasting requires to acquire knowledge about lifestyle and consumption patterns of residents. A new forecasting framework is proposed in this work which uses the extra appliance measurements in meter-level for short-term electrical load forecasting. The long short-term memory network as a deep learning method is used as a predictor where useful features are fed to it for forecast learning. A lagged load variable vector is assigned to each point of the load curve. To remove redundant details and to use the approximate component of the feature vector, the wavelet decomposition is applied to it. In addition, a new version of collaborative representation is introduced and used to achieve information of the neighboring points (previous and future time instances) of the considered load point. Collaborative representation of the feature vector associated with each load point contains valuable local information about adjacent load points. The load features extracted from the lagged load variable vector provide superior forecasting performance especially with extra appliances load data.

Suggested Citation

  • Imani, Maryam & Ghassemian, Hassan, 2019. "Residential load forecasting using wavelet and collaborative representation transforms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:86
    DOI: 10.1016/j.apenergy.2019.113505
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    Cited by:

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    3. Jiang, Weiheng & Wu, Xiaogang & Gong, Yi & Yu, Wanxin & Zhong, Xinhui, 2020. "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, Elsevier, vol. 193(C).
    4. 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).
    5. Ahajjam, Mohamed Aymane & Bonilla Licea, Daniel & Ghogho, Mounir & Kobbane, Abdellatif, 2022. "Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting," Applied Energy, Elsevier, vol. 326(C).
    6. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    7. 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.
    8. 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).
    9. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    10. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2020. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," Applied Energy, Elsevier, vol. 261(C).
    11. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
    12. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    13. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    14. Sun, Wenqiang & Wang, Zihao & Wang, Qiang, 2020. "Hybrid event-, mechanism- and data-driven prediction of blast furnace gas generation," Energy, Elsevier, vol. 199(C).
    15. Abdullah Alrasheedi & Abdulaziz Almalaq, 2022. "Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting," Mathematics, MDPI, vol. 10(15), pages 1-22, July.

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