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Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection

Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection

Yi Wang, Tianyu Wang, Wanyu Wang, Yiru Hou
Copyright: © 2024 |Volume: 36 |Issue: 1 |Pages: 25
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9798369324530|DOI: 10.4018/JOEUC.340385
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MLA

Wang, Yi, et al. "Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection." JOEUC vol.36, no.1 2024: pp.1-25. http://doi.org/10.4018/JOEUC.340385

APA

Wang, Y., Wang, T., Wang, W., & Hou, Y. (2024). Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection. Journal of Organizational and End User Computing (JOEUC), 36(1), 1-25. http://doi.org/10.4018/JOEUC.340385

Chicago

Wang, Yi, et al. "Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection," Journal of Organizational and End User Computing (JOEUC) 36, no.1: 1-25. http://doi.org/10.4018/JOEUC.340385

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Abstract

In the face of intensifying global climate change, carbon neutrality has emerged as a pivotal strategy to curb greenhouse gas emissions and confront the complexities associated with climate challenges. However, achieving carbon neutrality poses a formidable challenge: the identification and mitigation of anomalies within the carbon sequestration process. These anomalies can result in unintended carbon dioxide leakage, emissions, or system failures, thus jeopardizing the feasibility and resilience of carbon neutrality initiatives. This research introduces the ResNet-BIGRU-TPA network, an innovative model that integrates deep learning techniques with time series attention mechanisms. The primary focus centers on addressing the intricate task of anomaly detection within the realm of carbon offsetting, specifically aiming to enhance precision in identifying a wide array of complex anomalous events. Through rigorous experimental validation across four diverse datasets, the model has exhibited exceptional performance.