| Title |
Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources |
| ID_Doc |
69827 |
| Authors |
Anagnostopoulos, T; Kyriakopoulos, GL; Ntanos, S; Gkika, E; Asonitou, S |
| Title |
Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources |
| Year |
2020 |
| Published |
Sustainability, 12, 7 |
| DOI |
10.3390/su12072817 |
| 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. |
| Author Keywords |
intelligent predictive analytics; sustainable management; business investment; renewable energy sources; data mining |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000531558100242 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2071-1050/12/7/2817/pdf?version=1585823875
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