Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

Christil Pasion, Torrey Wagner, Clay Koschnick, Steven Schuldt, Jada Williams and Kevin Hallinan
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Christil Pasion: Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Torrey Wagner: Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Clay Koschnick: Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Steven Schuldt: Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Jada Williams: Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Kevin Hallinan: Department of Mechanical and Aerospace Engineering, University of Dayton, Dayton, OH 45469, USA

Energies, 2020, vol. 13, issue 10, 1-14

Abstract: Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R 2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R 2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R 2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.

Keywords: photovoltaics; solar panels; power prediction; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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