Predictive Analytics in Solar Energy : Optimizing Efficiency with AI

Robin Sarkar

doi:10.32628/CSEIT241051081

Authors

Robin Sarkar Enstall, USA Author

DOI:

https://doi.org/10.32628/CSEIT241051081

Keywords:

Predictive Analytics, Solar Energy, Artificial Intelligence, Machine Learning, Energy Optimization

Abstract

This article explores the transformative role of predictive analytics and artificial intelligence in the rapidly growing solar energy sector. It examines how these technologies revolutionize solar farm operations by enabling equipment failure prediction, performance optimization, intelligent maintenance scheduling, and enhanced grid integration. The article delves into the key components of predictive analytics systems, including data collection via IoT sensors, advanced data processing, machine learning models, and actionable insights generation. It also discusses the technical implementation challenges, such as ensuring data quality, improving model interpretability, integrating with legacy systems, and addressing the industry skill gap. By leveraging these cutting-edge technologies, solar energy providers can significantly improve operational efficiency, reduce costs, and maximize energy production, ultimately accelerating the global transition to renewable energy.

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References

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