The increase in the consumption of energy year after year emphasizes
the importance of power production by photovoltaic (PV) systems.
Despite an increase in the use of PV systems, accurate solar power [kWh] daily harvest predicting data are not readily available. Accurate predicted solar power data is necessary because the data is helpful to designers who need to optimally size a PV panel before installation. Moreover, accurately predicted max dc power can indicate whether the PV panel is operating efficiently and economically or not. This thesis develops an approach to predict max solar power based on a Linear Regression model. The approach, which ia a simple regression was implemented using measured data on a response variable, a max solar power (Pmax),
and predictor variables such as Global Horizontal (GH), Plane of Array (PA), Short Circuit
Current (Isc), Open Circuit Voltage (Voc), and Panel Temperature (Temp). The statistical results of the linear regression model
produced reasonable values which agreed with those of the measured
data from the solar panel. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2012-05-5090 |
| Date | 09 July 2012 |
| Creators | Kwon, Youngsung |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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