3D cloud detection and tracking system for solar forecast using multiple sky imagers
Abstract
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 »
- Authors:
-
- Stony Brook Univ., Stony Brook, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21); USDOE
- OSTI Identifier:
- 1193237
- Alternate Identifier(s):
- OSTI ID: 1245220
- Report Number(s):
- BNL-108159-2015-JA
Journal ID: ISSN 0038-092X
- Grant/Contract Number:
- SC00112704; AC02-98CH10886
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Solar Energy
- Additional Journal Information:
- Journal Volume: 118; Journal Issue: C; Journal ID: ISSN 0038-092X
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; sky imagery; cloud detecting; cloud tracking; short-term forecast
Citation Formats
Peng, Zhenzhou, Yu, Dantong, Huang, Dong, Heiser, John, Yoo, Shinjae, and Kalb, Paul. 3D cloud detection and tracking system for solar forecast using multiple sky imagers. United States: N. p., 2015.
Web. doi:10.1016/j.solener.2015.05.037.
Peng, Zhenzhou, Yu, Dantong, Huang, Dong, Heiser, John, Yoo, Shinjae, & Kalb, Paul. 3D cloud detection and tracking system for solar forecast using multiple sky imagers. United States. https://doi.org/10.1016/j.solener.2015.05.037
Peng, Zhenzhou, Yu, Dantong, Huang, Dong, Heiser, John, Yoo, Shinjae, and Kalb, Paul. Tue .
"3D cloud detection and tracking system for solar forecast using multiple sky imagers". United States. https://doi.org/10.1016/j.solener.2015.05.037. https://www.osti.gov/servlets/purl/1193237.
@article{osti_1193237,
title = {3D cloud detection and tracking system for solar forecast using multiple sky imagers},
author = {Peng, Zhenzhou and Yu, Dantong and Huang, Dong and Heiser, John and Yoo, Shinjae and Kalb, Paul},
abstractNote = {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.},
doi = {10.1016/j.solener.2015.05.037},
journal = {Solar Energy},
number = C,
volume = 118,
place = {United States},
year = {Tue Jun 23 00:00:00 EDT 2015},
month = {Tue Jun 23 00:00:00 EDT 2015}
}
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