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Title: 3D cloud detection and tracking system for solar forecast using multiple sky imagers

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteenmore » minutes.« less
 [1] ;  [1] ;  [2] ;  [2] ;  [2] ;  [2]
  1. Stony Brook Univ., Stony Brook, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0038-092X
Grant/Contract Number:
SC00112704; AC02-98CH10886
Accepted Manuscript
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 118; Journal Issue: C; Journal ID: ISSN 0038-092X
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21); USDOE
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; sky imagery; cloud detecting; cloud tracking; short-term forecast