ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction - Jiang, Wei; Liu, Changjiang; Qu, Qiang; Wang, Zhen; Hu, Liangnan; Xie, Zhaofu; Zhang, Bokun; He, Jingzhou (Frontiers in Ecology and Evolution, 2024)
Carbon neutrality has become one of the important strategies to address global climate change. However, there are currently some shortcomings in carbon emission predictions, which necessitate more efficient methods to tackle this challenge. Time series data is crucial in carbon emission forecasting as it reflects the changing trends of carbon emissions over time. To address this issue, we propose an artificial intelligence-based approach aimed at improving the accuracy and reliability of carbon emission predictions. Our model combines various techniques, including ISSA(Improved Sparrow Search Algorithm) and GRU-Transformer, to better handle time series data. These components play a critical role in the model, helping us capture and analyze the complexity of carbon emission data. Through experimental results, we validate the performance of the model. Our model undergoes thorough evaluations on multiple datasets and is compared to competing models in terms of performance. The experimental results demonstrate that our model excels in various performance metrics, including lower RMSE and MAE values and higher R2 scores, providing a powerful tool for improving carbon emission predictions. The significance of this research lies in introducing a new AI-based approach to the field of carbon emission prediction, which holds promise for providing more accurate data support for optimizing and implementing carbon neutrality measures. Furthermore, by enhancing the accuracy of time series predictions, we can better understand the trends in carbon emissions, thereby enabling more effective mitigation measures and contributing to the resolution of global climate change issues.