In-situ Atmospheric Intelligence for Hybrid Power Grids: Volume 3 (Analysis of Whole Sky Image Compression)
Abstract
Future Multi-Domain Operation battlefields require uninterrupted electrical power. To enable versatile and resilient resources, the integration and optimization of hybridized power are being investigated. The Atmospheric Intelligence for Hybrid Power Grids (AIHPG) Project is proactively exploiting atmospheric intelligence as part of this effort. One element of atmospheric intelligence comes from whole sky imagers (WSIs). As machine-learning models are applied, the need for additional WSI image data expands, creating a storage space challenge. This report documents research conducted to assess two image compression techniques (image resolution compression [with PNG lossless compression] and image detail compression [using JPEG format]), and three compressed-image applications (peak signal-to-noise ratio, solar radiation diagnostic model, and percent cloud cover assessment). Strengths and weaknesses for these methods and applications are characterized using samples of clear, partly cloudy, and overcast WSI images. The best choice was determined to be a function of the images application(s).
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2021
- Accession Number
- AD1156157
Entities
People
- Gail Tirrell Vaucher
- Hailey Goodman
- Michael S. Lee
Organizations
- United States Army Research Laboratory
- University of Central Florida