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

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

doi:10.1016/j.solener.2015.05.037

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

Journal Article · Tue Jun 23 04:00:00 EDT 2015 · Solar Energy

DOI:https://doi.org/10.1016/j.solener.2015.05.037· OSTI ID:1193237

Peng, Zhenzhou ^[1]; Yu, Dantong ^[1]; Huang, Dong ^[2]; Heiser, John ^[2]; Yoo, Shinjae ^[2]; Kalb, Paul ^[2]

Stony Brook Univ., Stony Brook, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States)

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 fifteen minutes.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)

OSTI ID:: 1193237

Alternate ID(s):: OSTI ID: 1245220

Report Number(s):: BNL--108159-2015-JA

Journal Information:: Solar Energy, Journal Name: Solar Energy Journal Issue: C Vol. 118; ISSN 0038-092X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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