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This research presents the development of linear regression models to predict horizontal photovoltaic power output. We collected a dataset from 14 global Department of Defense (DoD) installations over a timeframe of one year using an experimental apparatus, resulting in 24,179 usable data points. We developed a linear model to predict power output, which incorporated site-specific weather and geographical characteristics, along with Köppen-Geiger climate classifications in order to determine the effect of adding climate to the model. After performing a Wald test between the full model and a reduced model without Köppen-Geiger climate variables, it was determined that including Köppen-Geiger climate variables improved the model’s ability to account for horizontal photovoltaic power variation by 3%. Although adding Köppen-Geiger variables provided added value when modeling the training dataset, these variables were less effective in predicting the validation dataset. From the analysis, the ideal Köppen-Geiger region was determined to be a warm temperate main classification, a fully humid precipitation classification and a warm summer temperature classification. This region possessed a 30% greater average power production than the mean value of the base climate classification. We found that the cost-effectiveness of a photovoltaic array depends on Köppen-Geiger climate regions, in addition to weather characteristics and the orientation of the array.


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Journal of Energy and Natural Resources (e-ISSN 2330-7404)