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Lorry driver to be charged in connection with road death

The driver of a Toolsie Persaud Limited lorry is expected to be charged in connection with the death of pedestrian after he walked into the path of the vehicle on the Bagotstown Public Road, East B…

Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal Features

Accurate power load forecasting is essential for the efficient operation and planning of electrical grids, particularly given the increased variability and complexity introduced by renewable energy sources. This paper introduces GAT-LSTM, a hybrid model that combines Graph Attention Networks (GAT) and Long Short-Term Memory (LSTM) networks. A key innovation of the model is the incorporation of edge attributes, such as line capacities and efficiencies, into the attention mechanism, enabling it to dynamically capture spatial relationships grounded in grid-specific physical and operational constraints. Additionally, by employing an early fusion of spatial graph embeddings and temporal sequence features, the model effectively learns and predicts complex interactions between spatial dependencies and temporal patterns, providing a realistic representation of the dynamics of power grids. Experimental evaluations on the Brazilian Electricity System dataset demonstrate that the GAT-LSTM model significantly outperforms state-of-the-art models, achieving reductions of 21. 8% in MAE, 15. 9% in RMSE and 20. 2% in MAPE. These results underscore the robustness and adaptability of the GAT-LSTM model, establishing it as a powerful tool for applications in grid management and energy planning.

The Final Ursids Meteor Shower of This Decade Will Coincide With Winter Solstice This Weekend

This daily column at GNN.org features all the good news, anniversaries and notable birthdays from this day in history, the Winter Solstice—December 21.

A Gentle Introduction to Graph Neural Networks

What components are needed for building learning algorithms that leverage the structure and properties of graphs?

A Gentle Introduction to Graph Neural Networks

What components are needed for building learning algorithms that leverage the structure and properties of graphs?

Как мы в МТС создали библиотеку для работы с графовыми нейронными сетями

Привет, Хабр! Меня зовут Диана Павликова, я работаю ML-инженером. Часто к нам приходят задачи, когда нужно повысить качество работы модели там, где обычными способами это сделать уже не получается. Мы...