SolarProphet

Irradiance forecasting using features extracted from a sky-camera

General Information

Work on this project is supported by the Industry-Academia partnership "The Center for a Solar-Powered Future by 2050"

Getting Started

The included file sample_config.py should be renamed to config.py and should contain values for the neptune project name and api key. This file is included in the .gitignore manifest so that the API key is not committed to a repository by mistake.

Instructions assume a Windows OS with NVIDIA card. Mathods should be similar for other operating systems but may require some modifications. If running without a graphics card, the tensorflow installation will be different, and running the model will use the CPU.

Follow the instructions below once the prerequisites are met to create an environment with the required dependencies, or execute the create_env.sh script to automatically do the steps below for an NVIDIA GPU setup or create_env_NoGPU.sh for a CPU installation of TensorFlow.

Prerequisites

There may be problems with GPU dependencies on your system. These are the processes I used for my PC and M1 Mac, but your results may vary. For specifics on installation, especially tensorflow, see:

Windows Installation

Create conda environment named solarprophet with python 3.8:

conda create -n solarprophet python=3.8
conda activate solarprophet

Follow GPU Setup instructions from TensorFlow

conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
pip install --upgrade pip
# Anything above 2.10 is not supported on the GPU on Windows Native
pip install "tensorflow<2.11" 
# Check tensorflow connection to GPU
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Install remaining packages

pip install pandas seaborn Pillow scipy dask statsmodels tqdm neptune neptune-tensorflow-keras ipykernel joblib scikit-learn Jinja2
# pytables is a bit finnicky, needs to be installed and upgraded
pip install --user --upgrade tables

Linux/MacOS Installation

Create conda environment named solarprophet with python 3.8:

conda create -n solarprophet python=3.8
conda activate solarprophet

Install packages:

python -m pip install tensorflow

If using tensorflow-metal for Apple Silicon or AMD GPUs:

python -m pip install tensorflow-metal

Install remaining packages:

python -m pip  install pandas seaborn Pillow scipy dask statsmodels tqdm neptune neptune-tensorflow-keras ipykernel joblib scikit-learn Jinja2
conda install pytables

If tensorflow installation worked correctly, python tf_test.py should work and start a training bar