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AI-Based Automated Solar Farm Layout Optimization Using Satellite Imagery and Machine Learning

Vibhor Kumar

Abstract


The rapid expansion of renewable energy infrastructure requires efficient solar power plant design and planning methodologies. Traditional solar farm planning relies heavily on manual geographic information system (GIS) analysis and engineering expertise, which can be time consuming and computationally intensive. Recent advances in artificial intelligence and satellite remote sensing enable new possibilities for automated renewable energy planning.

This paper proposes an AI-based automated solar farm layout optimization framework that integrates satellite imagery analysis, machine learning-based land classification, and optimization algorithms to determine optimal photovoltaic panel placement. The system utilizes deep learning models for satellite image segmentation to identify suitable installation areas and applies optimization techniques to maximize energy production while reducing shading losses.

The proposed architecture provides a scalable solution for intelligent solar farm planning and demonstrates the potential of geospatial artificial intelligence in renewable energy infrastructure development.


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References


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