AI Methods and Deployment Challenges from Prediction to Trusted Operation in Renewable-Rich Smart Grids: A Review

Ganesh Kumar Gautam, Rajeev Thakur — Mon, 28 Sep 2026 00:00:00 +0000

The inclusion of renewable energy sources, distributed energy resources, and electric vehicles in the power grid has transformed conventional power grids into complex, multi-directional cyber-physical systems, for which traditional model-based tools are inadequate and/or linear. Artificial Intelligence (AI) and Machine Learning (ML) have therefore become key enablers of next-generation smart grids, with AI-driven data-based solutions helping forecast energy loads and renewable generation, diagnose faults and protect against them, optimise microgrids and electric-vehicle charging, and support cybersecurity and explainable decision-making. This work summarizes the current AI/ML applications for smart grid optimization and renewable-energy integration, based mainly on literature published from 2024 to 2026, including peer-reviewed studies and recent preprints. It presents recent breakthroughs in deep learning-based forecasting, reinforcement learning-based microgrid energy management, and digital-twin-based grid monitoring and AI-based cyber defense, and places these advances in the context of the fast-digitalizing power system in India as an illustrative example from emerging economies. There are persistent data quality issues, model interpretability concerns, real-time scalability issues, and AI-specific security concerns, which are identified, and directions for future research are proposed. The review aims to serve as a single reference to help researchers, utility engineers, and policymakers design "smart" power systems that are able to be powered by renewables.

An Intelligent Machine Learning Framework for Early Cardiovascular Disease Risk Prediction

Gagana G, Inchara Bhatta K. M, Kavana, Likitha H, Gnanamani H — Wed, 09 Sep 2026 00:00:00 +0000

Heart disease continues to be a major health problem across the world, and identifying it at an early stage can help improve treatment and reduce serious complications. In recent years, machine learning has become a useful approach for studying patient health records and predicting the possibility of heart disease. This survey reviews and compares different machine learning techniques such as Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost. It also looks at common data preparation methods, including data cleaning, feature selection, handling imbalanced datasets using SMOTE, and model tuning to improve prediction results. From the studies reviewed, ensemble-based methods like XGBoost and Gradient Boosting generally provide more consistent and accurate predictions than many traditional algorithms. This also discusses the advantages, limitations, and research opportunities in this area. Overall, the survey shows that machine learning can support doctors by providing faster and more reliable predictions, making early diagnosis of heart disease more effective.

Journal of Innovations in Data Science and Big Data Management

AI Methods and Deployment Challenges from Prediction to Trusted Operation in Renewable-Rich Smart Grids: A Review

An Intelligent Machine Learning Framework for Early Cardiovascular Disease Risk Prediction