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Data Augmentation Technique Based on Improved Time-Series Generative Adversarial Networks for Power Load Forecasting in Recirculating Aquaculture Systems

Author

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  • Jun Li

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Xingzhao Zhang

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Qingsong Hu

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Fuxi Zhang

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Oleg Gaida

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Leilei Chen

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

Abstract

Factory aquaculture faces a difficult situation due to its high running costs, with one of the main contributing factors being the high energy consumption of aquaculture workshops. Accurately predicting the power load of recirculating aquaculture systems (RAS) is critical to optimizing energy use, reducing energy consumption, and promoting the sustainable development of factory aquaculture. Adequate data can improve the accuracy of the prediction model. However, there are often missing and abnormal data in actual data detection. To solve this problem, this study uses a time-series convolutional network–temporal sequence generation adversarial network (TCN-TimeGAN) to synthesize multivariate RAS data and train a long short-term memory (LSTM) network on the original and generated data to predict future electricity loads. The experimental results show that the data generated based on the improved TCN-TimeGAN provide more comprehensive coverage of the original data distribution, with a lower discriminative score (0.2419) and a lower predictive score (0.0668) than the conventional TimeGAN. Using the generated data for prediction, the R 2 reached 0.86, which represents a 19% improvement over the ARIMA model. Meanwhile, compared to LSTM and GRU without data augmentation, the mean absolute error (MAE) was reduced by 1.24 and 1.58, respectively. The model has good prediction performance and generalization ability, which benefits the RAS energy saving, production planning, and the long term sustainability of factory aquaculture.

Suggested Citation

  • Jun Li & Xingzhao Zhang & Qingsong Hu & Fuxi Zhang & Oleg Gaida & Leilei Chen, 2024. "Data Augmentation Technique Based on Improved Time-Series Generative Adversarial Networks for Power Load Forecasting in Recirculating Aquaculture Systems," Sustainability, MDPI, vol. 16(23), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10721-:d:1538339
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    References listed on IDEAS

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    1. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
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    5. Chen-Yu Tai & Wun-Jhe Wang & Yueh-Min Huang, 2023. "Using Time-Series Generative Adversarial Networks to Synthesize Sensing Data for Pest Incidence Forecasting on Sustainable Agriculture," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
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