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


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.

Share and Cite:

Steffen, R. , Suk, S. , Ahn, Y. and Ford, G. (2013) Modeling Energy Generation by Grid Connected Photovoltaic Systems in the United States. Journal of Building Construction and Planning Research, 1, 39-44. doi: 10.4236/jbcpr.2013.12006.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. L. Lee and S. Y. Kim, “Enhanced Efficiency of Organic Photovoltaic Cells with Sr2SiO4:Eu2+ and SrGa2S4: Eu2+ Phosphors,” Organic Electronics, Vol. 14, No. 4, 2013, pp. 1021-1026. doi:10.1016/j.orgel.2013.01.032
[2] X. Zhang, X. Zhao, S. Smith, J. Xu and X. Yu, “Review of R&D Progress and Practical Application of the Solar Photovoltaic/Thermal (PV/T) Technologies,” Renewable and Sustainable Energy Reviews, Vol. 16, No. 1, 2012, pp. 599-617. doi:10.1016/j.rser.2011.08.026
[3] P. Biwole, P. Eclache and F. Kuznik, “Phase-Change Materials to Improve Solar Panel’s Performance,” Energy and Buildings, Vol. 62, 2013, pp. 59-67. doi:10.1016/j.enbuild.2013.02.059
[4] A. M. Paudel and H. Sarper, “Economic Analysis of a Grid-Connected Commercial Photovoltaic System at Colorado State University-Pueblo,” Energy, Vol. 52, 2013, pp. 289-296.
[5] P. Denholm and R. Margolis, “The Regional Per-Capita Solar Electric Footprint for the United States,” National Renewable Energy Laboratory, Golden, 2007. doi:10.2172/921203
[6] S. Danov, J. Carbonell, J. Cipriano and J. Martí-Herrero, “Approaches to Evaluate Building Energy Performance from Daily Consumption Data Considering Dynamic and Solar Gain Effects,” Energy and Buildings, Vol. 57, 2013, pp. 110-118. doi:10.1016/j.enbuild.2012.10.050
[7] J. K. Kaldellis and A. Kokala, “Quantifying the Decrease of the Photovoltaic Panels’ Energy Yield Due to Phenomena of Natural Air Pollution Disposal,” Energy, Vol. 35, No. 12, 2010, pp. 4862-4869. doi:10.1016/
[8] K. A. Moharram, M. S. Abd-Elhady, H. A. Kandil and H. El-Sherif, “Influence of Cleaning Using Water and Surfactants on the Performance of Photovoltaic Panels,” Energy Conversion and Management, Vol. 68, 2013, pp. 266-272. doi:10.1016/j.enconman.2013.01.022
[9] G. Boyle, “Renewable Energy: Power for A Sustainable Future,” Oxford University Press, Oxford, 2004.
[10] F. Cucchiella and I. D’Adamo, “Estimation of the Ener-Getic and Environmental Impacts of a Roof-Mounted Building-Integrated Photovoltaic Systems,” Renewable and Sustainable Energy Reviews, Vol. 16, No. 7, 2012, pp. 5245-5259. doi:10.1016/j.rser.2012.04.034
[11] NREL, “PVWatts Software,” National Renewable Energy Laboratory, Golden, 2013.
[12] D. Thevenard and S. Pelland, “Estimating the Uncertainty in Long-Term Photovoltaic Yield Predictions,” Solar Energy, Vol. 91, 2013, pp. 432-445. doi:10.1016/j.solener.2011.05.006
[13] T. Oozeki, T. Izawa, K. Otani and K. Kurokawa, “An Evaluation Method of PV Systems,” Solar Energy Materials and Solar Cells, Vol. 75, No. 3-4, 2003, pp. 687-695. doi:10.1016/S0927-0248(02)00143-5
[14] S. Mau and U. Jahn, “Performance Analysis of Grid-Connected PV Systems,” 21st European Photovoltaic Solar Energy Conference, Dresden, 4-8 September 2006.
[15] B. Marion, J. Adelstein, K. Boyle, H. Hayden, B. Hammond, T. Fletcher, B. Canada, D. Narang, D. Shugar, H. Wenger, A. Kimber, L. Mitchell, G. Rich and T. Townsend, “Performance Parameters for Grid-Connected PV Systems,” 31st IEEE Photovoltaics Specialists Conference and Exhibition, Lake Buena Vista, 3-7 January 2005, pp. 1601-1606.
[16] T. Hong, C. Koo and M. Lee, “Estimating the Loss Ratio of Solar Photovoltaic Electricity Generation through Stochastic Analysis,” ICCEPM, Garden Grove, 9-11 January 2013, pp. 389-399.
[17] R. O’Brien, “A Caution Regarding Rules of Thumb for Variance Inflation Factors,” Quality & Quantity, Vol. 41, No. 5, 2007, pp. 673-690. doi:10.1007/s11135-006-9018-6

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.