AI-enhanced generation- and load forecasts in isolated power systems

This paper presents an AI-driven approach to forecasting electricity demand and variable renewable generation in the Faroe Islands’ isolated power system, supporting the transition to 100% renewable electricity by 2030. The study employs automated machine learning to leverage SCADA (Supervisory Control and Data Acquisition) and weather data to generate accurate short-term forecasts, enabling efficient dispatch of generation and storage resources and facilitating dynamic pricing strategies that encourage electricity use when renewable output is high. The methodology addresses the technical challenges of balancing supply and demand in a high-renewables scenario, demonstrating that automated machine learning can simplify model development and improve forecast accuracy compared with classical…