Machine-Learning-Solar-Energy-Forecasting-README.md

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Machine-Learning-Solar-Energy-Forecasting-README.md

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Developed a machine learning model in MATLAB to forecast solar energy generation using historical weather data, supporting efficient renewable energy management.

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# Machine Learning-Based Solar Energy Forecasting Using MATLAB

## Overview

This project presents a machine learning-based framework for forecasting photovoltaic (PV) power generation using historical weather and environmental data. The forecasting model leverages key meteorological parameters, including temperature, humidity, cloud cover, solar radiation, and solar angle, to predict solar energy output with high accuracy. Developed in MATLAB, the framework enhances renewable energy planning, smart grid operation, and sustainable energy management through reliable solar power prediction.

## Objectives

- Develop an accurate machine learning model for solar energy forecasting.
- Analyze the impact of environmental parameters on PV power generation.
- Improve prediction reliability through data preprocessing and feature engineering.
- Support renewable energy integration and intelligent energy management.

## Key Features

- Machine learning-based solar power forecasting
- Historical weather data preprocessing
- Feature engineering and data normalization
- Performance evaluation using standard statistical metrics
- MATLAB implementation and visualization
- Renewable energy and smart grid applications

## Technologies Used

- MATLAB
- Machine Learning
- Data Analysis
- Renewable Energy Forecasting

## Dataset

The forecasting model was trained and evaluated using a dataset containing **100,000 historical observations**.

### Input Parameters
- Temperature
- Humidity
- Cloud Cover
- Solar Radiation
- Solar Angle

## Performance Evaluation

| Metric | Value |
|--------|--------:|
| Prediction Accuracy | **98.24%** |
| R² Score | **0.9824** |
| RMSE | **0.123 kW** |
| MAE | **0.087 kW** |
| MSE | **0.015** |

## Applications

- Solar Energy Forecasting
- Renewable Energy Management
- Smart Grid Optimization
- Energy Scheduling
- Sustainable Power Systems

## Future Enhancements

- Integration with real-time IoT sensor data
- Advanced deep learning models (LSTM, GRU, Transformer)
- Weather API integration
- Real-time forecasting dashboard
- Edge AI deployment for smart energy systems

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