An LSTM trained on CMIP6 wind speed and pressure data is claimed to outperform MLP and Transformer-LSTM models for simulating wind power in Germany, but the evaluation lacks metrics and uses a leakage-prone random split.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Climate Aware Deep Neural Networks (CADNN) for Wind Power Simulation
An LSTM trained on CMIP6 wind speed and pressure data is claimed to outperform MLP and Transformer-LSTM models for simulating wind power in Germany, but the evaluation lacks metrics and uses a leakage-prone random split.