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AI-Based Smart Monitoring and Optimization of Renewable Energy Power Plants
Abstract
As renewable energy installations grow rapidly across the world, managing them efficiently has become a real problem. Solar and wind plants are unpredictable by nature, and the traditional monitoring tools most operators still rely on were not built to handle that unpredictability. This paper describes a smart monitoring and optimization system that brings together machine learning, deep learning, IoT sensor networks, and edge computing to manage hybrid renewable plants with minimal human intervention. The system uses Long Short-Term Memory (LSTM) networks to forecast energy output, Convolutional Neural Networks to catch faults in PV modules from thermal images, and Reinforcement Learning to continuously tune operational settings for better yield. Data from irradiance sensors, temperature probes, wind instruments, inverters, and grid meters feeds into both edge nodes and a central AI engine that makes decisions in real time. A predictive maintenance module flags equipment problems up to 72 hours before they cause a breakdown, cutting unplanned downtime by roughly 43%. We tested the full system on a 2.5 MW grid-connected hybrid plant in Karnataka, India over 14 months. The results show an 18.7% gain in annual energy production, a 31.2% drop in operations and maintenance costs, and a mean absolute percentage error of 3.42% on 24-hour-ahead solar forecasts. The system is designed to work across different equipment makes and plant configurations, which makes it a practical option for operators looking to modernize without rebuilding from scratch.
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