A deep learning architecture for energy service demand estimation in transport sector for Shared Socioeconomic Pathways.

ABSTRACT: Meeting current global passenger and freight transport energy service demands accounts for 20% of annual anthropogenic CO₂ emissions, and mitigating these emissions remains a considerable challenge for climate policy. Pursuant to this, energy service demands play a critical role in the energy systems and integrated assessment models but fail to get the attention they warrant. This study introduces a novel custom deep learning neural network architecture (called TrebuNet) that mimics the physical process of firing a trebuchet to model the nuanced dynamics inherent in energy service demand estimation. Here we show, how TrebuNet is designed, trained, and used to estimate transport energy service demand. We find that the TrebuNet architecture shows superior performance compared with

SUBMITTER: Joshi S 

PROVIDER: S-EPMC9981557 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature