Presentations

Abstract

The increasing integration of variable renewable energy resources, such as wind and solar, in the power systems has brought substantial changes in the dynamics of modern power systems, making them increasingly reliant on weather conditions. In response to this transformation, accurate weather forecasting has become vital for energy planning and grid management. These forecasts serve as an essential input for downstream energy forecast models, empowering grid operators to anticipate fluctuations in renewable energy generation, maintain grid stability, optimize economic efficiency within energy markets, and effectively manage renewable energy resources.  Traditional weather prediction models, such as numerical weather prediction (NWP) model, rely on physical and mathematical principles to simulate future weather conditions, enabling forecasts up to 15 days in advance; however, their accuracy diminishes significantly for shorter timescales (less than a few hours) due to computational limitations and data assimilation delays. In recent years, with the rise of machine learning in weather forecasting, data-driven weather models leveraging reanalysis data have emerged as a potential avenue to address these challenges in the evolving energy landscape.  The ability of these models to offer frequent weather updates at a low computational cost and to capture the nonlinear and complex nature of relationships holds promise for enhancing the predictive capabilities of downstream energy tasks, including renewable energy forecasting.  This study investigates the input data requirements, computational efficiency, and accuracy of Pangu-Weather, a state-of-the-art data-driven weather model, in capturing meteorological variables essential for short-term renewable energy forecasting. We examine the challenges and benefits of deploying such models, as well as their future potential in enhancing predictive capabilities. Furthermore, we demonstrate their practical utility by (1) improving forecast timeliness and (2) analyzing correlations between input weather variables and forecasted renewable energy generation. To validate our findings, we conduct a case study using real-world wind generation data from the Belgium transmission system operator. Our results illustrate the effectiveness of data-driven weather models in enhancing the accuracy and timeliness of renewable power forecasts, emphasizing their significance in modern power system planning and operation.