# Dynamic Line Rating using Hyper-local Weather Predictions: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2405.12319 · HTML: https://mlsanity.com/paper/2405.12319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [Log-normal distribution based EMOS models for probabilistic wind speed forecasting](https://mlsanity.com/paper/1407.3252/) (2014)
- [Leveraging the Existing German Transmission Grid with Dynamic Line Rating](https://mlsanity.com/paper/2303.02987/) (2023)

## Abstract (arXiv)

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.