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A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting

Author

Listed:
  • Zaki Masood

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

  • Rahma Gantassi

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

  • Ardiansyah

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

  • Yonghoon Choi

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

Abstract

The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting the energy industry into a modern era of reliable and sustainable energy networks. This paper proposes a time-series clustering framework with multi-step time-series sequence to sequence (Seq2Seq) long short-term memory (LSTM) load forecasting strategy for households. Specifically, we investigate a clustering-based Seq2Seq LSTM electricity load forecasting model to undertake an energy load forecasting problem, where information input to the model contains individual appliances and aggregate energy as historical data of households. The original dataset is preprocessed, and forwarded to a multi-step time-series learning model which reduces the training time and guarantees convergence for energy forecasting. Furthermore, simulation results show the accuracy performance of the proposed model by validation and testing cluster data, which shows a promising potential of the proposed predictive model.

Suggested Citation

  • Zaki Masood & Rahma Gantassi & Ardiansyah & Yonghoon Choi, 2022. "A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting," Energies, MDPI, vol. 15(7), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2623-:d:786419
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    References listed on IDEAS

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