Modeling Future Energy Needs in a Changing Climate for Resilient and Sustainable Power Management

Addressing the complexities of energy management in the face of climate change requires innovative forecasting models that integrate varying environmental conditions and energy demands. This study focuses on Connecticut in the United States, using a historical dataset from 2011 to 2023, which includes data from ERA5 atmospheric reanalysis, the Automatic Surface Observation System (ASOS) network, and ISO New England (ISO-NE) energy demand records. Additionally, the study incorporates climate projections up to 2050, enhancing our understanding of long-term trends and variabilities affecting energy management. We employed a Long Short-Term Memory (LSTM) deep learning model to accurately predict future energy needs under different climate scenarios. Performance evaluations of the models were conducted using standard regression evaluation metrics, and our models demonstrated outstanding predictive capabilities, such as achieving a Mean Absolute Percentage Error (MAPE) of 2% and an R² value of 98%. These results underline the models’ precision and reliability in forecasting energy demand under various climate scenarios. The findings of this study offer valuable insights for policymakers, utility companies, and stakeholders, enabling more informed decision-making in the deployment and management of power grid. By accurately predicting future energy needs, regions can better prepare for and respond to climate change impacts, ensuring a resilient and sustainable power infrastructure.