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

Author

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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)

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.

Suggested Citation

  • Gi Yong Kim & Doo Sol Han & Zoonky Lee, 2020. "Solar Panel Tilt Angle Optimization Using Machine Learning Model: A Case Study of Daegu City, South Korea," Energies, MDPI, vol. 13(3), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:529-:d:311576
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    References listed on IDEAS

    as
    1. Benghanem, M., 2011. "Optimization of tilt angle for solar panel: Case study for Madinah, Saudi Arabia," Applied Energy, Elsevier, vol. 88(4), pages 1427-1433, April.
    2. Seul-Gi Kim & Jae-Yoon Jung & Min Kyu Sim, 2019. "A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    3. Chih-Chiang Wei, 2017. "Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan," Energies, MDPI, vol. 10(10), pages 1-26, October.
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    Cited by:

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    2. Ramez Abdallah & Emad Natsheh & Adel Juaidi & Sufyan Samara & Francisco Manzano-Agugliaro, 2020. "A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface," Energies, MDPI, vol. 13(23), pages 1-31, December.
    3. Sungha Yoon & Jintae Park & Chaeyoung Lee & Sangkwon Kim & Yongho Choi & Soobin Kwak & Hyundong Kim & Junseok Kim, 2023. "Optimal Orientation of Solar Panels for Multi-Apartment Buildings," Mathematics, MDPI, vol. 11(4), pages 1-16, February.
    4. Tong Liu & Li Liu & Yufang He & Mengfei Sun & Jian Liu & Guochang Xu, 2021. "A Theoretical Optimum Tilt Angle Model for Solar Collectors from Keplerian Orbit," Energies, MDPI, vol. 14(15), pages 1-17, July.
    5. Yasemin Ayaz Atalan & Abdulkadir Atalan, 2023. "Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    6. Ronewa Collen Nemalili & Lordwell Jhamba & Joseph Kiprono Kirui & Caston Sigauke, 2023. "Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models," Energies, MDPI, vol. 16(2), pages 1-19, January.
    7. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2021. "Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants," Energies, MDPI, vol. 14(11), pages 1-16, May.

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