Advancing the Vision of Carbon Neutrality: A Deep Learning Prediction Strategy with Attention Mechanism

Abstract

Carbon neutrality research, as a fundamental principle of environmental sustainability, has garnered widespread global attention. However, despite some progress, significant shortcomings persist. Current practices and methodologies in the field of carbon neutrality face numerous challenges and limitations, warranting further in-depth research and improvement. In this context, the importance of time-series data has become increasingly pronounced. Time-series data are crucial for understanding the carbon neutrality process, simulating future trends, and making precise predictions. To effectively harness this information, we propose an innovative TCN-BILSTM-Attention model that amalgamates temporal and spatial information with attention mechanisms to enhance our understanding and optimization of carbon neutrality strategies. Through extensive experimental validation, our research demonstrates the exceptional performance of the TCN-BILSTM-Attention model in the domain of carbon neutrality. Specifically, the proposed model outperforms existing approaches across four datasets (EPA, EIA, EEA, NREL). For instance, it achieved an accuracy of 97.53% on the EPA dataset and 96.12% on the EIA dataset. Overall, this study has significant implications not only for the practical application of carbon neutrality principles but also for providing novel perspectives and methodologies in global environmental sustainability and climate change mitigation. By offering innovative models and analytical tools for sustainable development, this work contributes valuable resources toward achieving carbon neutrality and advancing environmental conservation efforts.