https://github.com/adamouization/solar-irradiance-forecasting

Predicting short-term solar irradiance using deep learning and statistical methods on the Folsom dataset
https://github.com/adamouization/solar-irradiance-forecasting

arima arima-forecasting data-science deep-learning irradiance irradiance-forecasting machine-learning python

Last synced: about 2 months ago
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Predicting short-term solar irradiance using deep learning and statistical methods on the Folsom dataset

• Host: GitHub
• URL: https://github.com/adamouization/solar-irradiance-forecasting
• Owner: Adamouization
• License: agpl-3.0
• Created: 2023-08-18T07:53:25.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2023-09-07T10:23:21.000Z (almost 3 years ago)
• Last Synced: 2025-09-08T23:51:38.412Z (10 months ago)
• Topics: arima, arima-forecasting, data-science, deep-learning, irradiance, irradiance-forecasting, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 4.77 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Short-Term Solar Irradiance Forecasting using LSTMs ![GitHub issues](https://img.shields.io/github/issues/Adamouization/Solar-Irradiance-Forecasting) [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54) ![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white) ![Keras](https://img.shields.io/badge/Keras-%23D00000.svg?style=for-the-badge&logo=Keras&logoColor=white) ![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white) ![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white) ![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white) ![Matplotlib](https://img.shields.io/badge/Matplotlib-%23ffffff.svg?style=for-the-badge&logo=Matplotlib&logoColor=black) ![scikit-learn](https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge&logo=scikit-learn&logoColor=white)

___

## Project Goal

Solar energy is a rapidly growing source of renewable energy, contributing significantly to global sustainability efforts. It depends on solar irradiance, which is the amount of solar energy received per unit area, measured using GHI (global irradiance). Accurate solar irradiance forecasting is crucial for:

* optimising energy production

* designing, planning and operational management of solar energy farms.

The goal of this project is to build a predictive model that can accurately predict future irradiance.

The objective is to leverage the various historical data provided in the *"[A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods](https://zenodo.org/record/2826939)"* dataset to build a solution that can accurately forecast irradiance for the next 20 minutes.

## Preliminary LSTM result

![image](https://raw.githubusercontent.com/Adamouization/Solar-Irradiance-Forecasting/master/output/model_validation/lstm_v4_forecast_vs_actual.png)

## Setup

Create a virtual environment:

```

python -m venv /Solar-Irradiance-Forecasting

source /Solar-Irradiance-Forecasting/bin/activate

```

Install requirements:

```

cd Solar-Irradiance-Forecasting

pip install -r env/requirements-light.txt

```

Download data:

```

python src/utils/donwload_zenodo_data.py

```

Open relevant Jupyternotebooks in `src/`

## License 

* see [LICENSE](https://github.com/Adamouization/Solar-Irradiance-Forecasting/blob/master/LICENSE) file.

## Contact

* Email: adam[at]jaamour[dot]com

* Website: www.adam.jaamour.com

* Linktree: https://linktr.ee/adamouization