Volume 8, Issue 2, June 2019, Page: 77-86
Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions
Parker Alan Hines, Department of Systems Engineering & Management, Air Force Institute of Technology, Wright-Patterson Air Force Base, United States of America
Torrey John Wagner, Department of Systems Engineering & Management, Air Force Institute of Technology, Wright-Patterson Air Force Base, United States of America
Clay Michael Koschnick, Department of Systems Engineering & Management, Air Force Institute of Technology, Wright-Patterson Air Force Base, United States of America
Steven James Schuldt, Department of Systems Engineering & Management, Air Force Institute of Technology, Wright-Patterson Air Force Base, United States of America
Received: Apr. 15, 2019;       Accepted: May 23, 2019;       Published: Jun. 10, 2019
DOI: 10.11648/j.jenr.20190802.15      View  216      Downloads  17
Abstract
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.
Keywords
Köppen-Geiger, Photovoltaic Cells, Linear Regression, Renewable Energy, Energy Resilience
To cite this article
Parker Alan Hines, Torrey John Wagner, Clay Michael Koschnick, Steven James Schuldt, Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions, Journal of Energy and Natural Resources. Vol. 8, No. 2, 2019, pp. 77-86. doi: 10.11648/j.jenr.20190802.15
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
U. Kumar, K. Soon, M. Seyedmahmoudian, and S. Mekhilef, “Forecasting of photovoltaic power generation and model optimization : A review,” Renew. Sustain. Energy Rev., vol. 81, no. June 2017, pp. 912–928, 2018.
[2]
J. A. Vásquez, K. Brecl, and M. Topič, “Köppen -Geiger-Photovoltaic Climate Classification,” 7th World Conf. Photovolt. Energy Convers. Kona, Hawaii, 2018.
[3]
J. H. Nussbaum, “Analyzing the Viability of Photovoltaic Pavement Systems: A Study In Structural Testing Methods, Measuring Potential Power, and Quantifying the Risks of Implementation,” Air Force Institute of Technology, 2017.
[4]
C. J. Booker, “Analysis of Temperature and Humidity Effects on Horizontal Photovoltaic Panels,” Air Force Institute of Technology, 2018.
[5]
J. A. Applebee, “Determining the Viability and Efficiency of GP3L Photovoltaic System Study At Air Force Installations in Various Climate Regions Air Force Institute of Technology,” Air Force Institute of Technology, 2018.
[6]
M. Belda, E. Holtanová, T. Halenka, and J. Kalvová, “Climate classification revisited: From Köppen to Trewartha,” Clim. Res., vol. 59, no. 1, pp. 1–13, 2014.
[7]
M. Kottek, J. Grieser, C. Beck, B. Rudolf, and F. Rubel, “World map of the Köppen-Geiger climate classification updated,” Meteorol. Zeitschrift, vol. 15, no. 3, pp. 259–263, 2006.
[8]
C. Beck, J. Grieser, M. Kottek, F. Rubel, and B. Rudolf, “Characterizing global climate change by means of Koeppen climate classification,” Clim. Status Rep. 2005, p. 10, 2006.
[9]
D. C. Jordan, J. H. Wohlgemuth, and S. R. Kurtz, “Technology and climate trends in PV module degradation,” 27th Eur. Photovolt. Sol. Energy Conf. Exhib., pp. 3118–3124, 2012.
[10]
I. Mahlstein, J. S. Daniel, and S. Solomon, “Pace of shifts in climate regions increases with global temperature,” Nat. Clim. Chang., vol. 3, no. 8, pp. 739–743, 2013.
[11]
F. P. Tso, D. R. White, S. Jouet, J. Singer, and D. P. Pezaros, “The Glasgow raspberry Pi cloud: A scale model for cloud computing infrastructures,” Proc. - Int. Conf. Distrib. Comput. Syst., pp. 108–112, 2013.
[12]
V. Vujovic, V. Vujović, and M. Maksimović, “Raspberry Pi as a Wireless Sensor node : Performances and constraints Raspberry Pi as a Wireless Sensor Node : Performances and Constraints,” 2014 37th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2014 - Proc., pp. 26–30, 2014.
[13]
Y. Sharon, B. Khachatryan, and D. Cheskis, “Low current Hall Effect Sensor,” 2018.
[14]
NOAA, “National Oceanic and Atmospheric Administration.”.
[15]
M. A. Green, K. Emery, Y. Hishikawa, W. Warta, and E. D. Dunlop, “Solar cell efficiency tables (version 43),” Prog. Photovoltaics Res. Appllications, vol. 22, pp. 1–9, 2015.
[16]
National Optical Astronomy Observatory, “Recommended Light Levels (Illuminance) for Outdoor and Indoor Venues.”.
[17]
A. Ã. Virtuani, E. Lotter, and M. Powalla, “Influence of the light source on the low-irradiance performance of Cu (In, Ga) Se 2 solar cells,” vol. 90, pp. 2141–2149, 2006.
[18]
O. Zogou, “Experimental and computational investigation of the thermal and electrical performance of a new building integrated photovoltaic concept,” 2011.
[19]
E. Skoplaki and J. A. Palyvos, “On the temperature dependence of photovoltaic module electrical performance: A review of efficiency/power correlations,” Sol. Energy, vol. 83, no. 5, pp. 614–624, 2009.
[20]
M. M. Fouad, L. A. Shihata, and E. S. I. Morgan, “An integrated review of factors influencing the performance of photovoltaic panels,” Renew. Sustain. Energy Rev., vol. 80, pp. 1499–1511, 2017.
[21]
F. H. Gandoman, S. H. E. Abdel, N. Omar, A. Ahmadi, and F. Q. Alenezi, “Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects,” Renew. Energy, vol. 123, pp. 793–805, 2018.
[22]
C. J. Smith, J. M. Bright, and R. Crook, “Cloud cover effect of clear-sky index distributions and differences between human and automatic cloud observations,” Sol. Energy, vol. 144, pp. 10–21, 2017.
[23]
P. Kuhn et al., “Benchmarking three low-cost, low-maintenance cloud height measurement systems and ECMWF cloud heights against a ceilometer,” Sol. Energy, vol. 168, pp. 140–152, 2018.
[24]
J. Antonanzas, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas, “Optimal solar tracking strategy to increase irradiance in the plane of array under cloudy conditions: A study across Europe,” Sol. Energy, vol. 163, pp. 122–130, 2018.
[25]
D. King, W. Boyson, and J. Kratochvil, “Analysis of factors influencing the annual energy production of photovoltaic systems,” in Photovoltaic Specialists, IEEE Conference, 2002, pp. 1356–1361.
[26]
A. J. Gutiérrez-trashorras, E. Villicaña-ortiz, E. Álvarez-álvarez, J. M. González-caballín, J. Xiberta-bernat, and M. J. Suarez-lópez, “Attenuation processes of solar radiation. Application to the quantification of direct and diffuse solar irradiances on horizontal surfaces in Mexico by means of an overall atmospheric transmittance,” Renew. Sustain. Energy Rev., vol. 81, pp. 93–106, 2018.
[27]
A. Nagengast, C. Hendrickson, and H. Scott Matthews, “Variations in photovoltaic performance due to climate and low-slope roof choice,” Energy Build., vol. 64, pp. 493–502, 2013.
[28]
M. Kutner, C. Hachtsheim, J. Neter, and W. Li, Applied Linear Statistical Models, 5th ed. McGraw-Hill Irwin, 2005.
[29]
J. McClave, P. Benson, and T. Sincich, Statistics for Business and Economics, 12th ed. Pearson, 2014.
[30]
A. Zeileis, T. Lumley, S. Berger, and N. Graham, “Robust Covariance Matrix Estimators.” 2018.
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