Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions

Parker Alan Hines; Torrey John Wagner; Clay Michael Koschnick; Steven James Schuldt

doi:doi:10.11648/j.jenr.20190802.15

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Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions

Parker Alan Hines, Torrey John Wagner, Clay Michael Koschnick, Steven James Schuldt

Published in Journal of Energy and Natural Resources (Volume 8, Issue 2)

Received: 15 April 2019 Accepted: 23 May 2019 Published: 10 June 2019

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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.

Published in	Journal of Energy and Natural Resources (Volume 8, Issue 2)
DOI	10.11648/j.jenr.20190802.15
Page(s)	77-86
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Köppen-Geiger, Photovoltaic Cells, Linear Regression, Renewable Energy, Energy Resilience

References

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APA Style

Parker Alan Hines, Torrey John Wagner, Clay Michael Koschnick, Steven James Schuldt. (2019). Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions. Journal of Energy and Natural Resources, 8(2), 77-86. https://doi.org/10.11648/j.jenr.20190802.15

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ACS Style

Parker Alan Hines; Torrey John Wagner; Clay Michael Koschnick; Steven James Schuldt. Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions. J. Energy Nat. Resour. 2019, 8(2), 77-86. doi: 10.11648/j.jenr.20190802.15

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AMA Style

Parker Alan Hines, Torrey John Wagner, Clay Michael Koschnick, Steven James Schuldt. Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions. J Energy Nat Resour. 2019;8(2):77-86. doi: 10.11648/j.jenr.20190802.15

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@article{10.11648/j.jenr.20190802.15,
  author = {Parker Alan Hines and Torrey John Wagner and Clay Michael Koschnick and Steven James Schuldt},
  title = {Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions},
  journal = {Journal of Energy and Natural Resources},
  volume = {8},
  number = {2},
  pages = {77-86},
  doi = {10.11648/j.jenr.20190802.15},
  url = {https://doi.org/10.11648/j.jenr.20190802.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20190802.15},
  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.},
 year = {2019}
}

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TY  - JOUR
T1  - Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions
AU  - Parker Alan Hines
AU  - Torrey John Wagner
AU  - Clay Michael Koschnick
AU  - Steven James Schuldt
Y1  - 2019/06/10
PY  - 2019
N1  - https://doi.org/10.11648/j.jenr.20190802.15
DO  - 10.11648/j.jenr.20190802.15
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 77
EP  - 86
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20190802.15
AB  - 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.
VL  - 8
IS  - 2
ER  -

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Author Information

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

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APA Style

Parker Alan Hines, Torrey John Wagner, Clay Michael Koschnick, Steven James Schuldt. (2019). Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions. Journal of Energy and Natural Resources, 8(2), 77-86. https://doi.org/10.11648/j.jenr.20190802.15

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ACS Style

Parker Alan Hines; Torrey John Wagner; Clay Michael Koschnick; Steven James Schuldt. Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions. J. Energy Nat. Resour. 2019, 8(2), 77-86. doi: 10.11648/j.jenr.20190802.15

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AMA Style

Parker Alan Hines, Torrey John Wagner, Clay Michael Koschnick, Steven James Schuldt. Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions. J Energy Nat Resour. 2019;8(2):77-86. doi: 10.11648/j.jenr.20190802.15

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@article{10.11648/j.jenr.20190802.15,
  author = {Parker Alan Hines and Torrey John Wagner and Clay Michael Koschnick and Steven James Schuldt},
  title = {Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions},
  journal = {Journal of Energy and Natural Resources},
  volume = {8},
  number = {2},
  pages = {77-86},
  doi = {10.11648/j.jenr.20190802.15},
  url = {https://doi.org/10.11648/j.jenr.20190802.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20190802.15},
  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.},
 year = {2019}
}

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TY  - JOUR
T1  - Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions
AU  - Parker Alan Hines
AU  - Torrey John Wagner
AU  - Clay Michael Koschnick
AU  - Steven James Schuldt
Y1  - 2019/06/10
PY  - 2019
N1  - https://doi.org/10.11648/j.jenr.20190802.15
DO  - 10.11648/j.jenr.20190802.15
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 77
EP  - 86
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20190802.15
AB  - 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.
VL  - 8
IS  - 2
ER  -

Copy | Download