Enhancing Solar Power Reliability: AI-Driven Anomaly Detection for Fault Diagnosis and Performance Optimization

Reliable solar power generation is essential for industries relying on renewable energy to sustain operations efficiently. However, fluctuations in solar energy output due to environmental conditions, equipment wear, and system inefficiencies create challenges in maintaining a consistent power supply. An alloy manufacturing company facing unstable energy production has encountered difficulties in meeting production demands, emphasizing the need for an advanced anomaly detection and performance optimization system. Unidentified faults in solar infrastructure can lead to energy losses, decreased efficiency, and operational disruptions, negatively impacting overall industrial productivity. This study introduces an AI-powered anomaly detection framework designed to improve solar power reliability and performance. By leveraging machine learning models alongside real-time sensor data, historical power trends, and environmental metrics, the proposed system detects irregularities in energy output, identifies faults, and predicts potential failures before they cause significant disruptions. Utilizing time-series analysis and pattern recognition techniques, the model enables early fault detection, supports predictive maintenance, and minimizes operational risks. Additionally, the system provides data-driven insights to enhance energy distribution, ensuring maximum utilization of available solar resources. The experimental results confirm that AI-based anomaly detection significantly improves solar energy efficiency by reducing downtime, optimizing energy consumption, and ensuring stable industrial operations. The proposed intelligent monitoring system enhances renewable energy utilization while strengthening industries against power fluctuations. Implementing AI-driven solutions can facilitate the transition toward more efficient and sustainable energy management strategies. This research highlights the transformative impact of AI and data-driven methodologies in advancing solar energy infrastructure, contributing to long-term sustainability and energy security in industrial applications.