Editorial

Abstract

Modern energy systems are increasingly characterized by large-scale renewable integration, deep digitalization, and tight coupling between physical infrastructure and cyber intelligence. These trends have significantly amplified the volume, heterogeneity, and complexity of data generated across energy generation, transmission, distribution, and consumption. Data fusion, which integrates multi-modal, multi-source, and multi-scale information, has therefore become a foundational enabler for prediction, optimization, security, and resilience in modern energy systems. This Special Issue, entitled “Data Fusion in Modern Energy Systems” , brings together ten original research articles that collectively advance the state of the art in fusion-driven energy intelligence. The accepted contributions are organized into three major research directions: (i) predictive intelligence via spatio-temporal and multi-modal data fusion, (ii) fusion-driven optimization and operational decision-making, and (iii) trustworthy and resilient energy systems through cross-domain data fusion. Together, these works illustrate how data fusion is evolving from a supporting data-processing technique into a central paradigm for intelligent, secure, and resilient energy systems.