Renewable Energy Prediction Using Fuzzy-MCDM and Machine Learning Techniques: A Sustainable Approach

Le résumé fourni par la source

Sustainable Development Goal 7 is essential to solve the global energy crisis as it seeks to offer affordable and clean energy. This study proposes a novel hybrid prediction model for Renewable Energy selection that integrates the Machine Learning (ML) technique with the Fuzzy Multi-Criteria Decision-Making approach in a hesitant environment to meet SDG 7 by considering multiple sustainable factors or criteria. Five renewable energies; "Solar Photo-Voltaic, Solar Thermal, Concentrating Solar, Hydro (Mini & Small), and Bio-Energy" are considered for this research, available in Uttarakhand. The "Hesitant-Fuzzy Analytical Hierarchy Process" and "Hesitant-Fuzzy TOPSIS" are combined with ML using the Logistic Regression technique to train and develop the prediction model. The research can provide valuable insights for policymakers and investors in similar regions. Advanced ML techniques, such as deep learning and reinforcement learning, may enhance accuracy.

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