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This article presents the results of an analysis of hourly data obtained from forty-three photovoltaic (PV) systems installed in North America. Energy data collected from these systems were organized according to monthly output in an effort to identify factors which are effective in predicting energy generation. Independent variables such as system capacity, shading, longitude, latitude, seasonal variation, and orientation were considered. Multiple regression analysis was used to quantify the kilowatt-hours that can be expected from a change in the independent variables. Results show that all six independent variables are significant predictors which can be used in a regression model to estimate system output with a high level of confidence. The analysis shows that approximately 83% of the variation in the amount of energy generated monthly by the forty-three solar panels is explained by the independent variables and the derived equation. Results of the study may prove helpful to solar panel system users who may need to consider less than optimum conditions during a PV panel installation and service life.

Modeling Energy Generation by Grid Connected Photovoltaic Systems in the United States