download.py download a year's worth of data from NSRDB
Flags
--lat: Latitude (to avoid errors make sure this value is within the continental United States) [Required]--lon: Longitude (to avoid errors make sure this value is within the continental United States) [Required]--train-years: Comma separated value string with years to download training data from (1998-2017 according to the official NSRDB docs) [Required]--test-years: 'Comma separated value string with years to download testing data from (1998-2017 according to the official NSRDB docs) [Required]--interval: 30 or 60 minute interval data [default: 30]
train.py train a configurable RNN
Flags
--lat: Latitude [Required]--lon: Longitude [Required]--train-years: Comma separated value string of downloaded irradiance data [Required]--seq-length: How many data points are needed to make one prediction [default: 64]--batch-size: Batch size of the training data [default: 64]--model-name: Name of the saved model [default: model]--start-date: Start date if you want to slice [default: None]--end-date: End date if you want to slice [default: None]--hidden-size: How many hidden neurons per LSTM layer [default: 35]--num-layers: How many LSTM layers [default: 2]--dropout: Dropout rate [default: 0.3]--epochs: Number of epochs [default: 5]--lr: Beginning learning rate [default: 1e-2]--decay: Weight decay also known as L2 penalty [default: 1e-5]--step-size: Decays the learning rate of each parameter group by gamma every step_size epochs [default: 2]--gamma: Multiplicative factor of learning rate decay [default: 0.5]
evaluate.py evaluate and plot the irradiance forecast results of a trained model
Flags
--lat: Latitude [Required]--lon: Longitude [Required]--test-years: Comma separated value string of downloaded irradiance data [Required]--seq-length: How many data points are needed to make one prediction [default: 64]--model-name: Name of the saved model [default: model]--start-date: Start date if you want to slice [default: None]--end-date: End date if you want to slice [default: None]--hidden-size: How many hidden neurons per LSTM layer [default: 35]--num-layers: How many LSTM layers [default: 2]--plot: Should we plot the data [default: False]