Irradiance-RNN
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Recurrent neural network for forecasting solar irradiance :sunny:
Irradiance-RNN
Irradiance-RNN is a partially configurable Recurrent Neural Network (RNN) implementing Long-Short Term Memory (LSTM) layers designed to forecast solar irradiance.
Introduction
The RNN is implemented using PyTorch. The solar irradiance data comes from the National Solar Radiation Database (NSRDB).
Data Preprocessing
- Removal of entries with zero irradiance (early morning and night hours)
- Normalization between 1 and -1 (LSTM layers use hyperbolic tangent)
Results
![Results](https://github.com/antoninodimaggio/Irradiance-RNN/raw/master/docs/images/result.png)
Setup
Required
- Python 3.6 +
- Get an API key from NREL
- Add your API access details to
/Irradiance-RNN/config/config.example.json
- Remove
.example
from/Irradiance-RNN/config/config.example.json
git clone https://github.com/antoninodimaggio/Irradiance-RNN.git
cd Irradiance-RNN
pip install -r requirements.txt
Download Training and Testing Data
- Make sure that there is no space when using comma separated values
- Detailed information on each argument can be found here
- You could also use
python download.py -h
for help
python download.py --lat 33.2164 \
--lon -97.1292 \
--train-years 2010,2011 \
--test-years 2012,2013 \
--interval 30
Training
- It is advised to change the
--model-name
after each run, otherwise a model with the same name will be overwritten. - Detailed information on each argument can be found here
- You could also use
python train.py -h
for help
python train.py --lat 33.2164 \
--lon -97.1292 \
--train-years 2010,2011 \
--seq-length 64 \
--batch-size 64 \
--model-name model \
--start-date 2010-01-01 \
--end-date 2011-06-01 \
--hidden-size 35 \
--num-layers 2 \
--dropout 0.3 \
--epochs 5 \
--lr 1e-2 \
--decay 1e-5 \
--step-size 2 \
--gamma 0.5
Evaluation and Plotting
- Make sure that the arguments
--seq-length
,--model--name
,hidden-size
, and--num-layers
are the same as what was used to train your model - Detailed information on each argument can be found here
- You could also use
python evaluate.py -h
for help
python evaluate.py --lat 33.2164 \
--lon -97.1292 \
--test-years 2012 \
--seq-length 64 \
--model-name model \
--start-date 2012-05-01 \
--end-date 2012-06-01 \
--hidden-size 35 \
--num-layers 2 \
--plot
Attributions/Thanks
- This project would not be possible without the GPU access provided to me by Rutgers University's Department of Computer Science.
- The inspiration behind this project comes from Alzahrani et al. who provided an initial RNN structure for me to get started.