THE ROLE OF CLOUD COMPUTING IN SCALING PREDICTIVE MAINTENANCE FOR SOLAR FARMS
Keywords:
Cloud Computing In Renewable Energy, Predictive Maintenance Systems, Solar Farm Operations, Machine Learning Analytics, IoT Sensor IntegrationAbstract
This comprehensive article examines the transformative role of cloud computing in scaling predictive maintenance operations for utility-scale solar farms. The article investigates how cloud platforms like AWS SageMaker and Microsoft Azure Machine Learning are revolutionizing maintenance strategies through advanced data processing and machine learning capabilities. With solar PV capacity projected to reach 2,350 GW by 2027, the integration of cloud computing has become crucial for managing the complexity of modern solar installations. The article analyzes how these platforms process over 100,000 data points per second per facility, enabling high-precision monitoring of panel temperatures (±0.5°C accuracy) and power output fluctuations at millisecond intervals. Through case studies of implementations at Desert Sun Solar Farm and SolarTech Facilities, the research demonstrates significant operational improvements, including a 47% reduction in unexpected downtime and 32% decrease in maintenance costs. The article encompasses technical architecture, machine learning operations, and cost-benefit considerations, providing insights into both current capabilities and future developments in cloud-based solar farm maintenance.
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