We propose to use Aerosol Optical Depth (AOD) measurements as an early warning system, to cue on-site validation of the soiling station and any available meteorological (MET) station data to validate if a work order to clean deposited particulate matter is necessary. This would move from reactive maintenance â often weeks delayed, to proactive maintenance â getting ahead of the weather impact if possible. Through this research, we will develop a suite of algorithms and ultimately software tools, that fuse multiple public and private (GOES-R ABI AOD, and local site MET/soiling station) datasets with features like project economics, asset location, weather forecast, etc. to hasten solar service companyâs decision times with regard to cleaning and/or protecting renewable generation assets. Our approach is to fuse the data and then explore feature relationships (correlative, causal) in the data using Machine Learning pattern identification and relationship learning algorithms along with Machine Reasoning visual pattern detection and tracking, and then developing models to predict behaviors and outcomes of those relationships using Machine Learning, Modeling and Simulation, and Optimization techniques. Phase II allows a significant opportunity to tune the solution to other localized phenomena an approach closer to true predictive mainten
