MDL: Industrial carbon emission prediction method based on meta-learning and diff long short-term memory networks.

ABSTRACT: Greenhouse gas emissions, as one of the primary contributors to global warming, present an urgent environmental challenge that requires attention. Accurate prediction of carbon dioxide (CO2) emissions from the industrial sector is crucial for the development of low-carbon industries. However, existing time series models often suffer from severe overfitting when data volume is insufficient. In this paper, we propose a carbon emission prediction method based on meta-learning and differential long- and short-term memory (MDL) to address this issue. Specifically, MDL leverages Long Short-Term Memory (LSTM) to capture long-term dependencies in time series data and employs a meta-learning framework to transfer knowledge from multiple source task datasets for initializing the carbon emission pred

SUBMITTER: Li F 

PROVIDER: S-EPMC11379199 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature