A platform for research: civil engineering, architecture and urbanism
Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources
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.
Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources
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.
Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources
Theodoros Anagnostopoulos (author) / Grigorios L. Kyriakopoulos (author) / Stamatios Ntanos (author) / Eleni Gkika (author) / Sofia Asonitou (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
HARNESSING DATA ANALYTICS FOR SUSTAINABLE BUSINESS GROWTH IN THE U.S. RENEWABLE ENERGY SECTOR
BASE | 2024
|RENEWABLE ENERGY SOURCES: ESSENTIAL FOR SUSTAINABLE DEVELOPMENT
British Library Online Contents | 2000
|Sustainable development and renewable energy sources in milagros community
BASE | 2020
|