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Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources

Author

Listed:
  • Theodoros Anagnostopoulos

    (Department of Business Administration, University of West Attica, 12241 Athens, Greece
    Department of Infocommunication Technologies, ITMO University, St. Petersburg 197101, Russia)

  • Grigorios L. Kyriakopoulos

    (Electric Power Division, Photometry Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Stamatios Ntanos

    (Department of Business Administration, University of West Attica, 12241 Athens, Greece)

  • Eleni Gkika

    (Department of Business Administration, University of West Attica, 12241 Athens, Greece)

  • Sofia Asonitou

    (Department of Business Administration, University of West Attica, 12241 Athens, Greece)

Abstract

Willingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused on the respondents’ perceptions and offered benefits for wind energy, solar photovoltaics (PVs), small hydro parks and biomass investments. The classification algorithms of Bayesian Network classifier, Logistic Regression, Support Vector Machine (SVM), C4.5, k-Nearest Neighbors (k-NN) and Long Short Term Memory (LSTM) were used. The Bayesian Network classifier was the best method, with a prediction accuracy of 0.7942. The most important variables for the prediction of willingness to invest were the level of information, the level of acceptance and the contribution to sustainable development. Future studies should include data on state incentives and their impact on willingness to invest.

Suggested Citation

  • Theodoros Anagnostopoulos & Grigorios L. Kyriakopoulos & Stamatios Ntanos & Eleni Gkika & Sofia Asonitou, 2020. "Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources," Sustainability, MDPI, vol. 12(7), pages 1-11, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2817-:d:340387
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    References listed on IDEAS

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    8. Li, Baolin & Wang, Jia & Nassani, Abdelmohsen A. & Binsaeed, Rima H. & Li, Zeyun, 2023. "The future of Green energy: A panel study on the role of renewable resources in the transition to a Green economy," Energy Economics, Elsevier, vol. 127(PB).

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