Solar Irradiance Prediction System Using Machine Learning

Problem statement

Solar power generation is highly dependent on weather conditions and irradiance levels, which vary throughout the day. Poor forecasting of solar irradiance can lead to suboptimal planning of energy storage and grid balancing. A data-driven prediction system can help estimate solar output and improve scheduling for microgrids and rooftop solar installations.

Abstract

This project develops a solar irradiance prediction system that uses historical weather and irradiance data to forecast short-term solar power potential. Meteorological parameters such as temperature, humidity, cloud cover, wind speed and past irradiance values are used to train regression models like Random Forest, Gradient Boosting, or LSTM-based neural networks. The predicted irradiance can be translated into expected power output for a given photovoltaic (PV) panel setup. A web dashboard visualizes real-time readings and forecast curves, enabling better decision-making for energy management.

Components required

• Historical solar irradiance and weather dataset (from local station or open data)
• Python with Pandas, NumPy, Scikit-Learn and/or TensorFlow/Keras
• Optional local sensor node with light sensor (e.g., photodiode/TSL2561) for real-time data
• Web dashboard (Flask / Django / Node.js + React)
• Optional integration with a small PV panel and power meter

Block diagram

Working

First, historical datasets containing solar irradiance (W/m²) and associated weather parameters are collected and preprocessed by handling missing values, scaling features and creating time-based features (hour of day, day of year, etc.). Several regression models are trained and evaluated on this dataset; the best-performing model is selected and saved. During deployment, current or forecasted weather values are fed into the trained model to predict irradiance for the next few hours. By multiplying irradiance estimates with PV panel specifications and efficiency, expected power output is calculated. A dashboard shows live measurements (if sensors are present) along with the near-future forecast curve, helping operators plan storage usage and grid interaction.

Applications

• Rooftop solar plant planning and scheduling
• Microgrid and smart grid energy management
• Research on renewable energy forecasting
• Educational tool for solar PV performance analysis