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<p>Green energy projections can help meet rising energy needs, address climate change, and other challenges by forecasting future trends. This study uses data from 1965 to 2023 to predict American green energy production and consumption. The gated recurrent unit model was chosen because it shows the time-dependent structure in the data time series. This study utilized energy consumption and renewable generation sources from Kaggle, spanning from 1965 to 2022, and data from the Energy Institute website, covering the period from 2022 to 2023. The model has a mean absolute error of 0.0417 and 0.0341 for consumption and production, respectively, and a mean squared error of 0.0110 and 0.0083 for production. The GRU model achieves the highest accuracy, identifying green energy data trends with an RMSE of 0.1049 for consumption and 0.0912 for output. This study shows how this model predicts energy needs. It emphasizes the integration of renewable energy and innovation in resource distribution. The research says the Quest for More Sustainable energy systems must overcome predicted technical challenges. All stakeholders gain from improved energy management policies with this knowledge. The GRU model’s performance enables the incorporation of economic and meteorological data to enhance prediction accuracy and support global efforts to clean up the energy system.</p>

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Deep learning for green energy: predicting consumption and production trends across the Americas

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

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  • Yonghong Liu,
  • Javed Rashid,
  • Muhammad S. Saleem,
  • Sonia Ashfaq,
  • Muhammad Faheem

摘要

Green energy projections can help meet rising energy needs, address climate change, and other challenges by forecasting future trends. This study uses data from 1965 to 2023 to predict American green energy production and consumption. The gated recurrent unit model was chosen because it shows the time-dependent structure in the data time series. This study utilized energy consumption and renewable generation sources from Kaggle, spanning from 1965 to 2022, and data from the Energy Institute website, covering the period from 2022 to 2023. The model has a mean absolute error of 0.0417 and 0.0341 for consumption and production, respectively, and a mean squared error of 0.0110 and 0.0083 for production. The GRU model achieves the highest accuracy, identifying green energy data trends with an RMSE of 0.1049 for consumption and 0.0912 for output. This study shows how this model predicts energy needs. It emphasizes the integration of renewable energy and innovation in resource distribution. The research says the Quest for More Sustainable energy systems must overcome predicted technical challenges. All stakeholders gain from improved energy management policies with this knowledge. The GRU model’s performance enables the incorporation of economic and meteorological data to enhance prediction accuracy and support global efforts to clean up the energy system.