Can we trust explainable artificial intelligence in wind power forecasting?
Author:
Funder
Swiss Federal Institute of Technology
Swiss Federal Office of Energy
Publisher
Elsevier BV
Reference30 articles.
1. Online ensemble approach for probabilistic wind power forecasting;Krannichfeldt;IEEE Trans Sustain Energy,2022
2. WindGMMN: scenario forecasting for wind power using generative moment matching networks;Liao;IEEE Trans Artif Intell,2022
3. Continuous and distribution-free probabilistic wind power forecasting: a conditional normalizing flow approach;Wen;IEEE Trans Sustain Energy,2022
4. A physics-inspired neural network model for short-term wind power prediction considering wake effects;Guo;Energy,2022
5. Short-term offshore wind power forecasting - a hybrid model based on discrete wavelet transform (DWT), seasonal autoregressive integrated moving average (SARIMA), and deep-learning-based long short-term memory (LSTM);Zhang;Renew Energy,2022
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