Solar Panel Tilt Angle Optimization Using Machine Learning Model: A Case Study of Daegu City, South Korea

Gi Yong Kim, Doo Sol Han and Zoonky Lee
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Gi Yong Kim: Graduate School of Information, Yonsei University, Seoul 03722, Korea
Doo Sol Han: Graduate School of Information, Yonsei University, Seoul 03722, Korea
Zoonky Lee: Graduate School of Information, Yonsei University, Seoul 03722, Korea

Energies, 2020, vol. 13, issue 3, 1-13

Abstract: Finding optimal panel tilt angle of photovoltaic system is an important matter as it would convert the amount of sunlight received into energy efficiently. Numbers of studies used various research methods to find tilt angle that maximizes the amount of radiation received by the solar panel. However, recent studies have found that conversion efficiency is not solely dependent on the amount of radiation received. In this study, we propose a solar panel tilt angle optimization model using machine learning algorithms. Rather than trying to maximize the received radiation, the objective is to find tilt angle that maximizes the converted energy of photovoltaic (PV) systems. Considering various factors such as weather, dust level, and aerosol level, five forecasting models were constructed using linear regression (LR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and gradient boosting (GB). Using the best forecasting model, our model showed increase in PV output compared with optimal angle models.

Keywords: solar panel; machine learning; solar irradiance (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 (7)

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