Solar Irradiance Analysis Project

This project aims to analyze solar irradiance data from the year 2019. The provided dataset (2019.csv) contains measurements of solar irradiance recorded at regular intervals throughout the year.

Dataset Description

The dataset (2019.csv) includes the following columns:

• Timestamp: The date and time of the measurement.
• Solar Irradiance (W/m^2): The measured solar irradiance in watts per square meter.

Installation

To run the analysis, you will need Python and several libraries. You can install the required libraries using pip:

pip install pandas matplotlib
pip install tensorflow
pip install numpy

Usage

You can use the provided Jupyter notebook (solar_irradiance_analysis.ipynb) to perform various analyses on the solar irradiance data. The notebook includes code snippets and explanations to guide you through the process.

To get started, follow these steps:

1. Clone this repository:

   https://github.com/pt-kunal-mishra/solar-irradiance-forecasting.git

2. Navigate to the project directory:

   cd solar-irradiance-forecasting

3. Launch Jupyter notebook:

   jupyter notebook

4. Open the Solar_Irradiance_Forecasting_kunal_yash.ipynb notebook and execute the cells sequentially to analyze the data.

Analysis

The analysis includes but is not limited to:

• Visualizing the distribution of solar irradiance measurements.
• Identifying trends and patterns in solar irradiance over different time periods.
• Correlating solar irradiance with other relevant factors, if applicable.
• Generating insights and conclusions based on the analysis.

Contributing

Contributions to this project are welcome. Feel free to submit issues, suggest improvements, or open pull requests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

The dataset used in this project is sourced from Kaggle.com. We acknowledge their contribution to scientific research.