The original research and development in this dissertation contributes to the field of building performance by actively harnessing a wider spectrum of directional solar radiation for use in buildings. Solar radiation (energy) is often grouped by wavelength measurement into the spectra ultraviolet (UV), visible (light), and short and long-wave infrared (heat) on the electromagnetic spectrum. While some of this energy is directly absorbed or deflected by our atmosphere, most of it passes through, scatters about, and collides with our planet. Modern building performance simulations, tools, and control systems often oversimplify this energy into scalar values for light and heat, when in reality they are interrelated directional spectral quantities of energy that are diffused and attenuated by clouds before colliding with surfaces. In addition to this, live building monitoring and control systems in-the-wild often do not track the location of the sun, separate direct sun energy from scattered sky energy, account for overcast clouds, considering occluded energy, etc. The work in this dissertation provides building energy simulations and control systems with finer-grain control over lighting and heating in order to optimize energy use and improve occupant well-being. We first present a data-driven machine learned sky model for predicting spectral radiance, and show how this technique can be used to produce spectral radiance maps for the entire hemispherical sky. We then integrate these predicted spectral radiance maps and other validated predictions into a custom radiosity engine in order to predict spectral daylighting and heating energy in building interiors. Finally, we present the design and prototyping of a cyber-physical building control system that monitors the sky and occupants in order to harness natural light and heat more effectively. We present ongoing and future work recommendations, such as sky cover projections to help reduce cooling recovery costs, and the use of spectral radiance maps in physically-based rendering engines.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1850 |
| Date | 01 December 2021 |
| Creators | Del Rocco, Joseph |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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