Solar Energy Predictor

Welcome to the Solar Energy Predictor project! Its not very flashy nor does it explore any further robust prediction, but it can be helpful for solar farmers, if they want to predict solar irradiance (GHI). 🚀

---

Setup Instructions

1. Download Solar Data

To train the model, you'll need to obtain a solar dataset from the NREL Solar Data repository. Save this file in your project directory.

2. Create a config.py File

In the root directory, create a config.py file to store the following configuration variables:

PATH = ""         # Path to your solar data CSV file
KEY = ""          # API key for WeatherAPI
URL = ""          # URL endpoint for WeatherAPI
MODEL_PATH = ""   # Path to save or load the trained model

3. Install Dependencies

Ensure you have Python installed. Then, install the required dependencies:

pip install -r requirements.txt

4. Run Tests

Before diving in, test the setup to ensure the code is functioning as expected:

pytest

Note: Test should pass, that means that the model is trained. Please check your weather api/data/dataset otherwise.

5. Start the Server

To start the FastAPI server and access the prediction API:

uvicorn app:app --reload
Visit http://127.0.0.1:8000/ in your browser to use the web interface.

6. Use the Trained Model

If you already have a trained model, you can use it to make predictions. Place your model in the path specified in MODEL_PATH, and the server will load it for inference.

Video Preview

Known Issues

Some features may not work as expected. Testing is incomplete, and certain edge cases might not be handled. The model’s performance depends on the quality of the dataset and preprocessing. If you encounter problems, feel free to explore and tweak the code