Data-driven technologies in smart buildings offer significant opportunities to enhance energy efficiency, sustainability, and occupant comfort. However, the existing literature often lacks a holistic examination of the technological advancements, adoption barriers, and business models necessary to realize these benefits. To address this gap, this scoping review synthesizes current research on these technologies, identifies factors influencing their adoption, and examines supporting business models. Inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured search of the literature across four major databases yielded 112 relevant studies. The key technologies identified included big data analytics, Artificial Intelligence, Machine Learning, the Internet of Things, Wireless Sensor Networks, Edge and Cloud Computing, Blockchain, Digital Twins, and Geographic Information Systems. Energy optimization is further achieved through integrating renewable energy resources and advanced energy management systems, such as Home Energy Management Systems and Building Energy Management Systems. Factors influencing adoption are categorized into social influences, individual perceptions, cost considerations, security and privacy concerns, and data quality issues. The analysis of business models emphasizes the need to align technological innovations with market needs, focusing on value propositions like cost savings and efficiency improvements. Despite the benefits, challenges such as high initial costs, technical complexities, security risks, and user acceptance hinder their widespread adoption. This review highlights the importance of addressing these challenges through the development of cost-effective, interoperable, secure, and user-centric solutions, offering a roadmap for future research and industry applications.

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework’s ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade.

The growing frequency of extreme weather events and grid disruptions highlights the urgent need for resilient energy systems. Decentralized and autonomous, community microgrids offer reliable and adaptable solutions. However, existing research often isolates components or control methods, lacking a comprehensive synthesis of their interdependencies and resilience strategies. To address this gap, this study conducts a comprehensive scoping review to synthesize the current state of knowledge on community microgrids, focusing on their types, components, operational strategies, control methods, and performance indicators. The research identifies key microgrid subtypes, such as islanded, hybrid, multi-energy, and autonomous systems, and evaluates the role of critical components like energy storage systems and distributed energy resources in enhancing resilience. It also highlights performance indicators, including reliability, stability, and flexibility, that serve as benchmarks for resilience. A novel framework is proposed, integrating microgrid design and operational aspects into a cohesive model for resilience enhancement. This framework provides actionable insights for practitioners to optimize microgrid design, for policymakers to create adaptive regulations, and for researchers to address knowledge gaps in the field. The findings underscore the critical role of advanced control methodologies in improving adaptability and efficiency under diverse operational conditions. By addressing the lack of an integrated approach in the existing literature, this study contributes to advancing resilient energy systems, supporting the energy transition, and promoting energy security. Future research should validate the proposed framework through empirical studies and explore scalable, cost-effective solutions to enable widespread adoption.

Hospitals usually operate 24/7 and consume a large amount of electricity to provide service and deploy critical machines that require reliable electricity. To maintain the energy reliability, hospitals usually equip diesel electricity generators, which are expensive and produce greenhouse gas (GHG) emissions. Therefore, there are potentials for hospitals to improve energy efficiency and provide energy flexibility to the electricity grid. However, due to specific functions of hospital buildings, the general concept of bright green buildings does not fit hospital buildings. Therefore, this paper aims to define and discussion the concept of bright green hospitals. A case study of two hospitals in the Philippines is selected to investigate energy management practices and installed intelligent technologies in hospitals. The “bright green hospitals” improve occupants' comfort, reduce energy consumption and carbon emissions, and keep energy reliability with intelligent technologies. Meanwhile, stakeholders' involvement takes an important role in the bright green buildings. This paper also finds that political, economic, social and technological factors affect the energy management in hospitals.