Introduction to the Special Issue on Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems

Diverse energy and power systems have been playing a significantly critical role in the revolution of sustainable energy supply for the future, which have a great impact on energy resources and efficiencies.Due to the emerging artificial intelligence and machine learning, traditional modeling techniques in these energy systems have met challenges in still leveraging physics model and first principle-based approaches.Moreover, with the rapid development of hardware and computing techniques, new modeling approaches for energy systems have become more and more important for system design, integration, analysis, control, and management.A total of 18 were selected based on a robust peer-reviewed process.The 18 articles are authored by researchers from world-wide universities, and are worthwhile to explore and present and disseminate the most recent advances related to modeling theory, approaches, and applications of energy systems.The first paper "Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid" by Alatwi et al. [1], introduces a hybrid dual-loop control method to stabilize the DC-link in a DC microgrid.Simulation results demonstrate the superior performance of the proposed controller in a 33.3% reduction in the objective function.This reduction holds significant implications for developing hybrid distributed energy systems, fostering the widespread integration of renewable energy sources (RESs) globally. The second paper "Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers" byBañales et al. [2], proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons, by a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise (SME) customers.The application of the methodology includes selecting key parameters via grid search to ensure the robustness of the results.The third paper "Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation" by Binbusayyis and Sha [3], dedicates to predict stability from the smart grid stability prediction dataset using machine learning algorithms.The regression process is performed using Modified PSO optimized XGBoost Technique with dynamic inertia weight update.The hyperparameters of XGBoost are fine-tuned for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics which determine the efficacy of the system.The fourth paper "Enhancing Safety in Electric Vehicles: Multi-Tiered Fault Detection for Micro Short Circuits and Aging in Battery Modules" by Luo et al. [4], develops a multi-tiered fault detection algorithm for series-connected lithium-ion batteries.Simulations and experiments conducted under various levels of micro short circuits validate the effectiveness of the algorithm, demonstrating its ability to distinguish