Analysis of Solar Energy Production Using AI Predictions Based On Climatic Conditions

Abstract

Solar energy is recognized as one of the most reliable and environmentally friendly renewable resources. However, its production efficiency is highly sensitive to fluctuating climatic conditions such as solar irradiance, temperature, humidity, and cloud cover, which makes accurate energy prediction a significant challenge. To address this, we developed an AI-powered system based on a Raspberry Pi controller, integrated with solar panels, a Battery Management System (BMS), various sensors, and weather data APIs. The system collects real-time meteorological data along with historical solar generation information, which is then processed through machine learning models to predict energy output with enhanced accuracy. This improved prediction enables better energy utilization, optimized battery storage, and increased operational stability. In comparison to traditional forecasting methods, the AI-based solution provides superior adaptability, continuous learning capabilities, and scalability across diverse environments. The integration of AI with renewable energy infrastructure represents a significant advancement toward building intelligent, efficient, and sustainable energy systems for the future.