Cost optimization and risk management in biomass energy projects: a deep learning approach to predicting compost emissions

The development and implementation of renewable energy projects are increasingly important in response to global climate change. This paper addresses the challenges of cost management in renewable energy projects and focuses on biomass energy as a way to produce biomass energy. In the proposed model, the Informer predicts Global Warming Potential (GWP) emissions during the sedimentation process, in which the prediction accuracy is critical to cost management. In a typical emission mode, the proposed model can underestimate the asymmetric cost risk of emissions and present a cost optimization strategy based on forecasts. Combining advanced forecasting technology with existing cycle management, this paper provides a comprehensive and practical tool for biomass energy projects, which is featured with accurate emission forecasting for sustainability and economic benefits.