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A Machine Learning Approach for Investment Analysis in Renewable Energy Sources: A Case Study in Photovoltaic Farms

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  • Konstantinos Ioannou

    (Forest Research Institute, NAGREF, Hellenic Agricultural Organization Demeter, Vasilika, 57006 Thessaloniki, Greece)

  • Evangelia Karasmanaki

    (Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada, Greece)

  • Despoina Sfiri

    (Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada, Greece)

  • Spyridon Galatsidas

    (Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada, Greece)

  • Georgios Tsantopoulos

    (Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada, Greece)

Abstract

Farmland offers excellent conditions for developing solar energy while farmers seem to appreciate its notable revenues. The increasing adoption of photovoltaics (PVs) on farmland raises various concerns with the most important being the loss of productive farmland and the increased farmland prices, which may prevent young farmers from entering the farming occupation. The latter can threaten the future of agriculture in countries that are already facing the problem of rural population ageing. The aim of this paper is to examine the effect of crop type on farmers’ willingness to install photovoltaics on their farmland. To that end, this study applies four machine learning (ML) algorithms (categorical regression, decision trees and random forests, support vector machines) on a dataset obtained from a questionnaire survey on farmers in a Greek agricultural area. The results from the application of the algorithms allowed us to quantify and relate farmers’ willingness to invest in PVs with three major crop types (cotton, wheat, sunflower) which play a very important role in food security. Results also provide support for making policy interventions by defining the rate of productive farmland for photovoltaics and also for designing policies to support farmers to start and maintain farming operations.

Suggested Citation

  • Konstantinos Ioannou & Evangelia Karasmanaki & Despoina Sfiri & Spyridon Galatsidas & Georgios Tsantopoulos, 2023. "A Machine Learning Approach for Investment Analysis in Renewable Energy Sources: A Case Study in Photovoltaic Farms," Energies, MDPI, vol. 16(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7735-:d:1286371
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    References listed on IDEAS

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    1. Kostas Karantininis, 2017. "A New Paradigm for Greek Agriculture," Springer Books, Springer, number 978-3-319-59075-2, March.
    2. Matthias Studer & Gilbert Ritschard & Alexis Gabadinho & Nicolas S. Müller, 2011. "Discrepancy Analysis of State Sequences," Sociological Methods & Research, , vol. 40(3), pages 471-510, August.
    3. Izanloo, Milad & Aslani, Alireza & Zahedi, Rahim, 2022. "Development of a Machine learning assessment method for renewable energy investment decision making," Applied Energy, Elsevier, vol. 327(C).
    4. Kostas Karantininis, 2017. "Framing a New Paradigm for Greek Agriculture," Springer Books, in: A New Paradigm for Greek Agriculture, chapter 0, pages 89-108, Springer.
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    1. Piotr Pietrzak & Elżbieta Kacperska & Jakub Kraciuk & Katarzyna Łukasiewicz, 2025. "Publication Trends, Key Findings, and Research Gaps in Renewable Energy Applications in Agriculture," Energies, MDPI, vol. 18(2), pages 1-22, January.

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