paper
2016
Applications & impacts
New model or method
RNN / LSTM / ConvLSTMGAN or VAE6 h–3 days Conference
2016-10-01
028
Oct 2016
André Gensler, Janosch Henze, Bernhard Sick et al.
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Applies an autoencoder with LSTM to solar power forecasting; the energy target is not an atmospheric target, so it is out of scope.
Applications & impactsClassic
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
In one sentence. Applies an autoencoder with LSTM to solar power forecasting; the energy target is not an atmospheric target, so it is out of scope.
Labels
- Topic
- Applications & impacts
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, GAN or VAE
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- arXiv:2016.78446 DOI:10.1109/smc.2016.7844673 OpenAlex:W2587586954
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.