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Performance Estimation Modeling via Machine Learning of an Agrophotovoltaic System in South Korea

Author

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  • Sojung Kim

    (Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Korea)

  • Sumin Kim

    (Department of Environmental Horticulture & Landscape Architecture, College of Life Science & Biotechnology, Dankook University, Cheonan-si 31116, Korea)

Abstract

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model.

Suggested Citation

  • Sojung Kim & Sumin Kim, 2021. "Performance Estimation Modeling via Machine Learning of an Agrophotovoltaic System in South Korea," Energies, MDPI, vol. 14(20), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6724-:d:657668
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    References listed on IDEAS

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    5. Schindele, Stephan & Trommsdorff, Maximilian & Schlaak, Albert & Obergfell, Tabea & Bopp, Georg & Reise, Christian & Braun, Christian & Weselek, Axel & Bauerle, Andrea & Högy, Petra & Goetzberger, Ado, 2020. "Implementation of agrophotovoltaics: Techno-economic analysis of the price-performance ratio and its policy implications," Applied Energy, Elsevier, vol. 265(C).
    6. Jaejin Hwang & Jinwon Lee & Kyung-Sun Lee, 2021. "A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-12, February.
    7. Sojung Kim & Burchan Aydin & Sumin Kim, 2021. "Simulation Modeling of a Photovoltaic-Green Roof System for Energy Cost Reduction of a Building: Texas Case Study," Energies, MDPI, vol. 14(17), pages 1-13, September.
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    Cited by:

    1. Youngjin Kim & Yeongjae On & Junyong So & Sumin Kim & Sojung Kim, 2023. "A Decision Support Software Application for the Design of Agrophotovoltaic Systems in Republic of Korea," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    2. Sojung Kim & Youngjin Kim & Youngjae On & Junyong So & Chang-Yong Yoon & Sumin Kim, 2022. "Hybrid Performance Modeling of an Agrophotovoltaic System in South Korea," Energies, MDPI, vol. 15(18), pages 1-13, September.
    3. Mamun, Mohammad Abdullah Al & Dargusch, Paul & Wadley, David & Zulkarnain, Noor Azwa & Aziz, Ammar Abdul, 2022. "A review of research on agrivoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Lee, Sangik & Lee, Jong-hyuk & Jeong, Youngjoon & Kim, Dongsu & Seo, Byung-hun & Seo, Ye-jin & Kim, Taejin & Choi, Won, 2023. "Agrivoltaic system designing for sustainability and smart farming: Agronomic aspects and design criteria with safety assessment," Applied Energy, Elsevier, vol. 341(C).
    5. Sojung Kim & Sumin Kim, 2023. "Economic Feasibility Comparison between Building-Integrated Photovoltaics and Green Systems in Northeast Texas," Energies, MDPI, vol. 16(12), pages 1-14, June.
    6. Quetzalcoatl Hernandez-Escobedo & David Muñoz-Rodríguez & Alejandro Vargas-Casillas & José Manuel Juárez Lopez & Pilar Aparicio-Martínez & María Pilar Martínez-Jiménez & Alberto-Jesus Perea-Moreno, 2022. "Renewable Energies in the Agricultural Sector: A Perspective Analysis of the Last Three Years," Energies, MDPI, vol. 16(1), pages 1-17, December.
    7. Kim, Sumin & Kim, Sojung, 2023. "Optimization of the design of an agrophotovoltaic system in future climate conditions in South Korea," Renewable Energy, Elsevier, vol. 206(C), pages 928-938.

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