☀️ AI-Based Solar Power Generation Prediction Using Machine Learning

📌 Project Overview

This project is a Machine Learning-based web application that predicts solar power generation (kW) using weather and atmospheric parameters. The application helps estimate the expected power output of a solar power plant based on environmental conditions.

The model is trained using historical weather and solar power generation data and is deployed using Flask.

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🚀 Features

• Predict solar power generation using weather parameters.
• Machine Learning model built using XGBoost Regressor.
• Data preprocessing and feature selection.
• User-friendly web interface built with HTML, CSS, and Flask.
• Fast and accurate predictions.
• Ready for deployment on Render.

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🛠️ Technologies Used

Programming Language

• Python

Machine Learning

• Scikit-learn
• XGBoost

Data Analysis

• Pandas
• NumPy

Data Visualization

• Matplotlib
• Seaborn

Web Development

• Flask
• HTML
• CSS

Model Deployment

• Joblib
• Git
• GitHub
• Render

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📂 Project Structure

Solar-Power-Prediction/
│
├── model/
│   ├── xgboost_model.pkl
│   └── feature_names.pkl
│
├── static/
│   └── style.css
│
├── templates/
│   └── index.html
│
├── train.py
├── app.py
├── requirements.txt
├── README.md
└── dataset.zip

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📊 Dataset

The dataset contains historical weather and solar power generation data.

Target Variable

• generated_power_kw

Selected Input Features

• Angle of Incidence
• Total Cloud Cover
• Zenith Angle
• Azimuth Angle
• Shortwave Radiation
• Total Precipitation
• Low Cloud Cover
• Wind Gust

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🔍 Machine Learning Workflow

1. Data Collection
2. Data Cleaning
3. Exploratory Data Analysis (EDA)
4. Feature Selection
5. Train-Test Split
6. Model Training
7. Model Evaluation
8. Hyperparameter Tuning
9. Model Saving
10. Flask Web Application
11. Deployment

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🤖 Models Compared

• Linear Regression
• Decision Tree Regressor
• XGBoost Regressor ✅ (Best Model)

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📈 Model Evaluation

The following metrics were used:

• R² Score
• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)

Among all the trained models, XGBoost Regressor achieved the best overall performance and was selected for deployment.

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💻 Installation

Clone the repository

https://github.com/kesharisneha340-blip/Solar-Power-Prediction

Move into the project folder

cd solar-power-prediction

Install dependencies

pip install -r requirements.txt

Run the application

python app.py

Open your browser

http://127.0.0.1:5000

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📷 Application Workflow

User Inputs Weather Parameters
            │
            ▼
Flask Web Application
            │
            ▼
Load Trained XGBoost Model
            │
            ▼
Predict Solar Power Generation
            │
            ▼
Display Prediction

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🌍 Real-World Applications

• Solar Power Plants
• Smart Grid Systems
• Renewable Energy Forecasting
• Energy Management
• Sustainable Energy Planning
• Smart Cities

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📌 Future Improvements

• Real-time weather API integration.
• Automatic weather data collection.
• Interactive dashboard.
• Prediction history.
• Mobile-friendly interface.
• Cloud deployment with Docker and CI/CD.

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Live Demo

https://solar-power-prediction-orc1.onrender.com/

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👩‍💻 Developed By

Sneha

Machine Learning & AI Enthusiast

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⭐ Acknowledgement

This project was developed as part of an internship to demonstrate the complete Machine Learning workflow, including data preprocessing, model training, evaluation, web application development, and deployment.