A Study on Applying Predictive Maintenance Techniques to Minimize Downtime and Maximize Energy Production Amongst Solar Consumers with Special Reference to Amravati City

The adoption of solar energy has increased significantly in India due to rising electricity costs, government incentives, and growing environmental awareness. However, many solar consumers face challenges such as unexpected system failures, reduced energy output, and high maintenance costs due to traditional reactive or preventive maintenance approaches. Predictive maintenance, supported by data analytics, sensors, and monitoring systems, offers a proactive solution by forecasting potential failures before they occur. This study focuses on the application of predictive maintenance techniques to minimize downtime and maximize energy production among solar energy consumers in Amravati City. Primary data was collected through a structured questionnaire from residential, commercial, and institutional solar users, while secondary data was gathered from journals, reports, and industry publications. The study analyzes common causes of solar system downtime, awareness of predictive maintenance practices, and their impact on operational efficiency. The findings reveal that predictive maintenance significantly improves system reliability, reduces breakdown-related losses, and enhances overall energy generation. However, lack of awareness, initial investment cost, and limited technical support act as barriers to adoption. The study provides practical suggestions for solar consumers, service providers, and policymakers to promote data-driven maintenance strategies for sustainable solar energy utilization.