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An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting

Author

Listed:
  • Yamin Shen

    (School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China)

  • Yuxuan Ma

    (School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China)

  • Simin Deng

    (School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China)

  • Chiou-Jye Huang

    (Department of Data Science and Big Data Analytics, Providence University, Taichung 43301, Taiwan)

  • Ping-Huan Kuo

    (Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
    Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Chiayi 62102, Taiwan)

Abstract

Electricity load forecasting is one of the hot concerns of the current electricity market, and many forecasting models are proposed to satisfy the market participants’ needs. Most of the models have the shortcomings of large computation or low precision. To address this problem, a novel deep learning and data processing ensemble model called SELNet is proposed. We performed an experiment with this model; the experiment consisted of two parts: data processing and load forecasting. In the data processing part, the autocorrelation function (ACF) was used to analyze the raw data on the electricity load and determine the data to be input into the model. The variational mode decomposition (VMD) algorithm was used to decompose the electricity load raw-data into a set of relatively stable modes named intrinsic mode functions (IMFs). According to the time distribution and time lag determined using the ACF, the input of the model was reshaped into a 24 × 7 × 8 matrix M , where 24, 7, and 8 represent 24 h, 7 days, and 8 IMFs, respectively. In the load forecasting part, a two-dimensional convolutional neural network (2D-CNN) was used to extract features from the matrix M . The improved reshaped layer was used to reshape the extracted features according to the time order. A temporal convolutional network was then employed to learn the reshaped time-series features and combined with the fully connected layer to complete the prediction. Finally, the performance of the model was verified in the Eastern Electricity Market of Texas. To demonstrate the effectiveness of the proposed model data processing and load forecasting, we compared it with the gated recurrent unit (GRU), TCN, VMD-TCN, and VMD-CNN models. The TCN exhibited better performance than the GRU in load forecasting. The mean absolute percentage error (MAPE) of the TCN, which was over 5%, was less than that of the GRU. Following the addition of VMD to the TCN, the basic performance of the model was 2–3%. A comparison between the SELNet model and the VMD-TCN model indicated that the application of a 2D-CNN improves the forecast performance, with only a few samples having an MAPE of over 4%. The model’s prediction effect in each season is discussed, and it was found that the proposed model can achieve high-precision prediction in each season.

Suggested Citation

  • Yamin Shen & Yuxuan Ma & Simin Deng & Chiou-Jye Huang & Ping-Huan Kuo, 2021. "An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1694-:d:493434
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    References listed on IDEAS

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    1. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac & Valentin A. Boicea, 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study," Energies, MDPI, vol. 14(13), pages 1-19, July.
    2. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Yuxuan Ma, 2022. "An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    3. Pedro M. R. Bento & Jose A. N. Pombo & Maria R. A. Calado & Silvio J. P. S. Mariano, 2021. "Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting," Energies, MDPI, vol. 14(21), pages 1-21, November.
    4. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.

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