Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources

Theodoros Anagnostopoulos, Grigorios L. Kyriakopoulos, Stamatios Ntanos, Eleni Gkika and Sofia Asonitou
Additional contact information
Theodoros Anagnostopoulos: Department of Business Administration, University of West Attica, 12241 Athens, Greece
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

Sustainability, 2020, vol. 12, issue 7, 1-11

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.

Keywords: intelligent predictive analytics; sustainable management; business investment; renewable energy sources; data mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/7/2817/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/7/2817/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:7:p:2817-:d:340387

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().