Using a sUAS to Calculate Solar Power Potential at Ohio Wesleyan
Students: Gram Dick ’22 and Jay McConkey ’23
Research Mentor: Nathan Rowley (OWU Department of Environment & Sustainability)
Power outages are a significant inconvenience on the Ohio Wesleyan campus that could be mitigated if the University opted to utilize solar panels. In this work, we use a Phantom 4 drone to capture images of all campus buildings to generate 2D and 3D representations. Using Pix4D and ArcGIS software programs, we are able to estimate the amount of sunlight each rooftop receives on the summer (maximum) and winter (minimum) solstices.
Over the past two years (2019-2021), Ohio Wesleyan University has experienced 120 hours of power outages (roughly 50% greater than the state of Ohio average). This causes a lack of productivity, leads to food waste, and hurts the university’s reputation.
To address the significant number of power outages at Ohio Wesleyan’s campus, renewable energies seem to be the ideal solution. As such, larger-scale projects, like Google’s Project Sunroof have generated a first-cut set of resources that can guide small-scale (e.g., private homes and Ohio Wesleyan University) implementation of renewable energy sources, like solar power. Project SunRoof provides users with straightforward information like displaying optimal placement for solar panel installation based on a building’s areas receiving the highest insolation (incoming solar radiation). However, there are limitations to Google’s methodology and product. For example, Project SunRoof only covers densely populated urban areas but has not yet reached suburban and rural areas like Delaware, Ohio (and the Ohio Wesleyan campus). In addition to the lack of coverage, Project Sunroof relies on satellite imagery, which has a coarse spatial resolution (on the order of 15 m per pixel). To increase the availability of data and to increase the spatial resolution, we (Remote Sensing Laboratory; RSL) apply a similar methodology to that of Project SunRoof, but our use of small unmanned aerial vehicles (sUAS) increases the resolution significantly to the scale of 1 inch. This in turn provides immensely improved accuracy in both the visible map and, more importantly, the three-dimensionality of our data products.