Cal Poly is home to a solar farm designed to nominally generate 4.5 MW of electricity. The Gold Tree Solar Farm (GTSF) is currently the largest photovoltaic array in the California State University (CSU) system, and it was claimed to be able to produce approximately 11 GWh per year. These types of projections come from power generation models which have been developed to predict power production of these large solar fields. However, when it comes to near-term forecasting of power generation with variable sources such as wind and solar, there is definitely room for improvement.
The two primary factors that could impact solar power generation are shading and the angle of the sun. The angle of the sun relative to GTSF’s panels can be analytically calculated using geometry. Shading due to cloud coverage, on the other hand, can be very difficult to map. Due to this, artificial neural networks (NN) have a lot of potential for accurate near-term cloud coverage forecasting. Much of the necessary training data (e.g. wind speeds, temperature, humidity, etc.) can be acquired from online sources, but the most important dataset needs to be captured at GTSF: sky images showing the exact location of the clouds over the solar field. Therefore, a new image capturing digital acquisition (DAQ) system has been implemented to gather the necessary training data for a goal of forecasting cloud coverage 15-30 minutes into the future.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3896 |
| Date | 01 June 2021 |
| Creators | Montgomery, Weston C |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
Page generated in 0.1316 seconds