Machine Learning-Based Predictive Analytics for Sustainable Renewable Energy Investments

Abstract

Renewable energy investments are crucial to address climate change effectively, reduce environmental impacts, and promote sustainable economic growth globally. Investing in renewable energy markets, however, presents many challenges due to their inherent complexity, market volatility, regulatory uncertainties, and unpredictability in technological advancements. This study was conducted to examine how machine learning-based predictive analytics can assist in making sustainable investments in renewable energy sources. This work evaluates the performance of multiple classifiers, including Logistic Regression, SVM, C4.5, KNN, LSTM, and Bayesian Networks, using metrics like prediction accuracy and class distribution analysis. According to this study, advanced investment strategies in the renewable energy sector can be significantly optimized by employing sophisticated predictive models, such as Long Short-Term Memory (LSTM) networks and Bayesian networks. The authors emphasize the critical importance of developing intelligent data-driven decision-making frameworks capable of effectively addressing class imbalance challenges, enhancing data quality, and delivering precise, actionable insights to facilitate strategic investments that accelerate global renewable energy adoption.