Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network

Han, Yongming; Cao, Lei; Geng, Zhiqiang; Ping, Weiying; Zuo, Xiaoyu; Fan, Jinzhen; Wan, Jing; Lü, Gang

doi:10.1016/j.scitotenv.2022.160410

Cited by 34 publications

(4 citation statements)

References 33 publications

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“…ResNet-152 is a deep CNN architecture and is part of the ResNet (residual network) series of models. It is renowned for its depth and efficiency in training deep neural networks for image classification tasks (Han et al, 2023). The fundamental principle behind ResNet-152 involves the use of residual blocks.…”

Section: Resnet Modelmentioning

confidence: 99%

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

Wang,

Wang

et al. 2024

Journal of Organizational and End User Computing

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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.

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Section: Resnet Modelmentioning

confidence: 99%

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

Wang,

Wang

et al. 2024

Journal of Organizational and End User Computing

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“…Some scholars have modified and improved model performance by adding or studying other variables. For example, Han and others [10] fully considered the factor of carbon dioxide to improve the accuracy of regional GDP prediction. Dai et al [11] compared the effects of introducing nighttime light data into linear regression, exponential, and complex artificial neural network models in GDP prediction, demonstrating the impact of nighttime light data on GDP prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel combination machine learning model for regional GDP prediction: evidence from China

Xia

2023

Atlantis Highlights in Engineering

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In recent years, the regional GDP prediction has become an efficient tool to coordinate economic development. This paper aims to study the regional GDP prediction and build a novel machine learning model with taking the entropy method into consideration to predict the future GDP values. This research uses the entropy method to calculate weights of the linear regression model and XGBoost regression model, then build a novel combination model to predict the GDP value of different regions. The model will combine the advantages of trend prediction from linear regression models and high fitting accuracy from XGBoost regression models. In the empirical analysis, the paper used the China's GDP values of different provinces in mainland and built the novel combination model with entropy method, linear regression model and XGBoost regression model. The results reveal that the proposed novel combination model outperforms the tow base models on the mean absolute percentage error evaluation metric.

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“…Deep residual networks show good performance for user identification of smartwatch users by complex hand movements [17]. Improved residual networks are used for economic and carbon emission forecasting in different regions to improve the accuracy of forecasting [18]. The attentional residual network was applied to hydroacoustic target recognition, and the average recognition accuracy was improved [19].…”

Section: Related Workmentioning

confidence: 99%

Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification

Zhang

2023

HSET

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Sepsis is a highly lethal disease in intensive care units, and patient indicators are constantly changing, making accurate prediction of patient mortality crucial for doctors to develop appropriate treatment plans. While machine learning and deep learning have been applied to sepsis research, model generalization performance can suffer from underfitting or gradient issues. To address these challenges, we propose using a deep residual network and a deep residual network incorporating Bayesian data prior to predict patient mortality using the MIMIC-III dataset. The accuracy of the two models on the validation set reached 0.9392 and 0.9329, respectively.

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Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network

Cited by 34 publications

References 33 publications

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

A novel combination machine learning model for regional GDP prediction: evidence from China

Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification

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