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Found 4 papers, 1 papers with code
It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future.
Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks
The results showed that the two Bayesian models outperformed the GAN model with regards to mean absolute errors (MAE), with the GNN architecture yielding the best results.
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
Various alterations have been proposed to better facilitate time series forecasting, of which this study focused on the Informer, LogSparse Transformer and Autoformer.
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
+2
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms.
