Predicting Optimizing Sensor Placement for Cost-Effective and Efficient Energy Storage Asset Monitoring The placement of field sensors on energy storage assets poses significant challenges, particularly when cost is a critical factor for project success or when the technology is still reaching commercial maturity. The current approach to sensor placement is riddled with obstacles that impede performance optimization and hinder the enhancement of monitoring capabilities. A primary pain point is the absence of a systematic and data-driven methodology for sensor placement. Operators often rely on intuition or ad-hoc methods, leading to suboptimal sensor locations. This results in blind spots and gaps in monitoring coverage, which limit the ability to detect potential issues, optimize resource allocation, and make informed decisions. With cost being a crucial factor, ensuring efficient placement becomes even more imperative to achieve project success and meet budget constraints. The complex interplay of various factors further complicates sensor placement determination. Environmental conditions, asset characteristics, risk profiles, and operational requirements all influence the optimal placement of sensors. However, the current manual assessment and decision-making processes struggle to effectively analyze and weigh these multifaceted factors. As a result, inefficient sensor placement occurs, compromising monitoring efficiency and hindering the ability to achieve the desired performance levels. Additionally, the dynamic nature of clean energy assets and their operational environments presents an ongoing challenge for sensor placement. Changes in asset configuration, emerging risks, and fluctuating environmental conditions require continuous monitoring, assessment, and adjustment. Without a solution that enables adaptable and optimized sensor placement, operators struggle to maintain effective monitoring capabilities over time, limiting the potential benefits of energy storage assets. To overcome these challenges, there is a critical need for a systematic, data-driven approach to sensor placement in energy storage asset monitoring. By leveraging advanced technologies such as AI, generative models, and data analytics, operators can optimize their sensor placement strategies, achieve comprehensive monitoring coverage, and enhance the efficiency of asset management practices. These innovative solutions will allow stakeholders to navigate the cost challenges and complexities associated with sensor placement, leading to improved operational efficiency, risk mitigation, and the maximization of clean energy asset performance. By addressing the pain points surrounding sensor placement, stakeholders can unlock the full potential of energy storage asset monitoring. With innovative solutions that offer cost-effective and efficient sensor placement, operators can achieve project success, advance technology maturity, and contribute to a sustainable and resilient clean energy future.