Data-driven prediction and uncertainty quantification of PWR Crud-Induced Power Shift using convolutional neural networks

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134447 - 134447

Published: Jan. 1, 2025

Language: Английский

---

Estimation of CO2 Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach

Systems, Journal Year: 2025, Volume and Issue: 13(3), P. 194 - 194

Published: March 11, 2025

This study focuses on estimating transportation system-related emissions in CO2 eq., considering several socioeconomic and energy- transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, deep learning algorithms. case of Turkey was considered as an example. Model performance evaluated using a dataset Turkey, future projections were made based scenario analysis compatible with Turkey’s climate change mitigation strategies. also adopted type-based analysis, exploring the role road, air, marine, rail systems. findings this indicate that aforementioned models can be effectively implemented to predict transport emissions, concluding they have valuable practical applications field.

Language: Английский

---

Peak in-cylinder pressure virtual sensor based on hybrid modeling framework

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136027 - 136027

Published: April 1, 2025

Language: Английский

---

Enhanced insulator fault detection using optimized ensemble of deep learning models based on weighted boxes fusion

International Journal of Electrical Power & Energy Systems, Journal Year: 2025, Volume and Issue: 168, P. 110682 - 110682

Published: April 26, 2025

Language: Английский

---