Prediction of Wind Direction Variability in Dynamic Thermal Rating with Decision Trees

Dynamic thermal rating (DTR) systems provide the means to actively monitor the capacity of the overhead transmission line and optimise its operation. They use physics models and weather measurements with a temporal resolution of a few minutes to calculate the thermal rating, i.e., the transfer capabilities of power lines. We have found in our research that this resolution is too coarse for wind speed and direction and that using higher-resolution wind data significantly alters the results of the DTR calculations. The discrepancy is correlated with the variability in wind direction, which is generally not known. This paper makes an attempt to predict wind direction variability, employing machine learning, specifically decision trees. We use the available 5 -minute weather measurements and features constructed from high-resolution 1 -second wind measurements as input parameters. We investigate several scenarios with varying input parameters and show that wind speed is the most significant standard available measurement. Our best prediction has a RMSE of 19.2°, MAE of 13.0° and MAPE of 0.21. We also gain some insights into the problem that we hope to explore further in the future.