"Solar Energy Prediction" is a data science project aimed at forecasting solar energy production using machine learning algorithms. The repository contains code for generating a synthetic dataset and implementing the Random Forest Regression algorithm for prediction.🌞🔬
This repository contains the code and dataset for a machine learning project focused on predicting solar energy production. Solar energy prediction plays a crucial role in optimizing the efficiency and management of solar power plants. By leveraging machine learning algorithms and historical weather data, this project aims to forecast solar energy output with high accuracy.
The dataset used in this project consists of various environmental parameters such as date, time, elevation, temperature, humidity, solar irradiance, cloud cover, wind speed, and solar production. These parameters are essential for training machine learning models to predict solar energy generation. The dataset is synthetic and generated for demonstration purposes.
- Python script to generate a synthetic dataset for solar energy prediction.
- Utilizes the pandas library for efficient data manipulation.
- Generates random values for environmental parameters to simulate real-world conditions.
- Outputs the dataset to a CSV file for further use in machine learning models.
- Python script demonstrating the implementation of a machine learning algorithm for solar energy prediction.
- Utilizes the scikit-learn library, a powerful tool for machine learning tasks in Python.
- Implements the Random Forest Regression algorithm, suitable for predicting continuous numerical values.
- Trains the model on the generated dataset and evaluates its performance using the mean squared error (MSE) metric.
- Clone the repository:
git clone https://github.com/yourusername/solar-energy-prediction.git
- Navigate to the project directory:
cd solar-energy-prediction
- Follow the instructions provided in each code file to generate the dataset and train the machine learning model.