  Dynamic Line Rating (DLR) systems are crucial for renewable energy
integration in transmission networks. However, traditional methods relying on
sensor data face challenges due to the impracticality of installing sensors on
every pole or span. Additionally, sensor-based approaches may struggle
predicting DLR in rapidly changing weather conditions. This paper proposes a
novel approach, leveraging machine learning (ML) techniques alongside
hyper-local weather forecast data. Unlike conventional methods, which solely
rely on sensor data, this approach utilizes ML models trained to predict
hyper-local weather parameters on a full network scale. Integrating
topographical data enhances prediction accuracy by accounting for landscape
features and obstacles around overhead lines. The paper introduces confidence
intervals for DLR assessments to mitigate risks associated with uncertainties.
A case study from Estonia demonstrates the practical implementation of the
proposed methodology, highlighting its effectiveness in real-world scenarios.
By addressing limitations of sensor-based approaches, this research contributes
to the discourse of renewable energy integration in transmission systems,
advancing efficiency and reliability in the power grid.
