Enhancing day ahead point forecasts with additional GIS information and a sequential dual LSTM approach

Increasing amounts of energy need to be transported through the electric grids leading to congestion and load-shedding.

Dynamic line rating (DLR) is a cost-effective method that allows transmission system operators to increase the current carrying capacities of electric grids beyond fixed limitations. This helps to reduce congestion and load-shedding. DLR considers environmental conditions, such as the weather, to ensure the safe and optimal operation of the grid. By implementing DLR, the grid can effectively transport increasing amounts of energy while minimizing the risk of disruptions.

Line ratings are commonly determined using meteorological data collected from weather stations near transformer stations. While these measurements offer localized insights into prevailing conditions, they may suffer from sensor inaccuracies and can be insufficient given the extensive distribution network area. Consequently, supplementary data from advanced weather models like ICON-D2 is utilized to offer a comprehensive overview of day-ahead weather conditions spanning the grids’s topology. However, despite the D2 model’s resolution of approximately 2.2 km, its granularity may prove inadequate for capturing nuanced, small-scale variations critical for predicting extreme values.

This study sought to investigate whether machine learning techniques could leverage the advantages of both traditional forecasting methods and modern data-driven approaches to deliver accurate predictions at the station level.

Our approach employs a dual two-step LSTM prediction methodology. Initially, GIS data such as fractional land cover or a topographic position index (TPI) are integrated with climatological information to generate forecasts for the target time series. Subsequently, the output of the first LSTM network undergoes a second training loop, where actual weather forecasts from the weather model are incorporated and aligned with the measurement data.

Our focus primarily centered on properties like wind speed and temperature, given their greater influence on the heating and cooling of power lines. We explored various network configurations and experimented with different initialization schemes, facilitating adjustments for extreme values to enhance balance within the system.

We conducted a comparative analysis by juxtaposing our predictions with baseline outcomes derived from the error between day-ahead forecasts generated by the weather model at the weather station. The results revealed an improvement, showcasing an 11% reduction in Root Mean Square Error (RMSE) across the board.

Our findings demonstrate the robust efficacy of our method, presenting substantial enhancements with minimal preprocessing and training requirements. This resilience ensures uninterrupted network operation, even in scenarios where stations may fail or be unavailable at specific grid points. Ultimately, our approach contributes to boosting the current carrying capacity within the power grid. By enabling more accurate assessment of future meteorological conditions, our method facilitates improved planning and optimization of energy transportation, thereby enhancing grid reliability and efficiency.