Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM.

ABSTRACT: As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of a

SUBMITTER: Qi X 

PROVIDER: S-EPMC10909168 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature