PVCalc

PVCalc is a Python application that analyzes and calculates solar energy potential production based on forecast data from geographical inputs. It integrates various data sources and uses the pvlib Python library for solar irradiance modeling.

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Features

• Retrieve and display weather data for selected locations worldwide.
• Calculate solar irradiance and other relevant solar energy metrics.
• Support for dust surface density input to refine calculations.
• Interactive GUI built with Tkinter.
• Data sourced from reputable dataset on world cities.
• Power production visualization for selected time periods.

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Installation

Before running the application from source make sure you have python 3.8+ installed, if not you can install python from Python Download.

1. Clone the repository

git clone https://github.com/spyridouladev/PVCalc.git
cd PVCalc

2. Create and activate virtual enviroment (recommended)

python -m venv venv

source venv/bin/activate      # On Linux/macOS
venv\Scripts\activate.bat     # On Windows
venv\Scripts\Activate.ps1     # On Powershell

3. Upgrade pip and install dependencies

python.exe -m pip install --upgrade pip
pip install -r requirements.txt

4. Run the application

python main.py

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Usage

First, input the data for the PV farm you want to calculate for.
Keep in mind that the weather data is based on forecasts, so it will not be 100% accurate.

Example: 3-Day Power Production

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Fixed Angle Mode

You can choose between fixed angle and single-axis tracker.

Fixed angle input interface:

Graph for fixed angle:

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Single-Axis Tracker Mode

Tracking input interface:

Graph for tracking:

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Results and Forecast

Estimated production (⚠️ Not precise due to limited forecast data):

Live weather data display beneath input fields:

Attribution and License

Cell Temperature model

This project incorporates concepts and correlation models derived from:

Gholami, A., Ameri, M., Zandi, M., Gavagsaz Ghoachani, R., Jafarzadegan Gerashi, S., Kazem, H. A., & Al-Waeli, A. H. A. (2023). Impact of harsh weather conditions on solar photovoltaic cell temperature: Experimental analysis and thermal-optical modeling. Solar Energy, 252, 176–194. https://doi.org/10.1016/j.solener.2023.01.039

Data Sources (licensed under CC BY 4.0)

• Basic World Cities Database by SimpleMaps, licensed under Creative Commons Attribution 4.0 (CC BY 4.0). Source: https://simplemaps.com/data/world-cities
• Population density data from The Center for International Earth Science Information Network (CIESIN), Columbia University, licensed under CC BY 4.0. Source: Gridded Population of the World, Version 4 (GPWv4)

Please review these licenses for detailed terms and attribution requirements.

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Software Library (licensed under BSD 3-Clause)

This project uses pvlib python, licensed under the BSD 3-Clause License. The copyright and license notice for pvlib are included with this project.