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.

Multi-agent systems are well-known for their expressiveness to explore interactions and knowledge representation in complex systems. Multi-agent systems have been applied in the energy domain since the 1990s. As more applications of multi-agent systems in the energy domain for advanced functions, the interoperability raises challenge raises to an increasing requirement for data and information exchange between systems. Therefore, the application of ontology in multi-agent systems needs to be emphasized and a systematic approach for the application needs to be developed. This study aims to investigate literature on the application of ontology in multi-agent systems within the energy domain and map the key concepts underpinning these research areas. A scoping review of the existing literature on ontology for multi-agent systems in the energy domain is conducted. This paper presents an overview of the application of multi-agent systems (MAS) and ontologies in the energy domain with five aspects of the definition of agent and MAS; MAS applied in the energy domain, defined ontologies in the energy domain, MAS design methodology, and architectures, and the application of ontology in the MAS development. Furthermore, this paper provides a recommendation list for the ontology-driven multi-agent system development with the aspects of 1) ontology development process in MAS design, 2) detail design process and realization of ontology-driven MAS development, 3) open standard implementation and adoption, 4) inter-domain MAS development, and 5) agent listing approach.

Buildings as prosumers have an important role in the energy aggregation market due to their potential flexible energy consumption and distributed energy resources. However, energy flexibility provided by buildings can be very complex and depend on many factors. The immaturity of the current aggregation market with unclear incentives is still a challenge for buildings to participate in the aggregation market. However, few studies have investigated business models for building participation in the aggregation market. Therefore, this paper develops four business models for buildings to participate in the energy aggregation market: (1) buildings participate in the implicit Demand Response (DR) program via retailers; (2) buildings with small energy consumption participate in the explicit DR via aggregators; (3) buildings directly access the explicit DR program; (4) buildings access energy market via Virtual Power Plant (VPP) aggregators by providing Distributed Energy Resources (DER)s. This paper also determines that it is essential to understand building owners’ needs, comforts, and behaviours to develop feasible market access strategies for different types of buildings. Meanwhile, the incentive programs, national regulations and energy market structures strongly influence buildings’ participation in the aggregation market. Under the current Nordic market regulation, business model one is the most feasible one, and business model two faces more challenges due to regulation barriers and limited monetary incentives.

Due to the complexity of business ecosystems, the architecture of business ecosystems has not been well discussed in the literature, and modeling or simulation of business ecosystems has been rarely focused. Therefore, this paper proposes a business ecosystem ontology and introduces a methodology for business ecosystem architecture design. The proposed methodology includes five stages: 1) Boundary identification of a business ecosystem; 2) Identification of actors and their roles in the business ecosystem; 3) Identification of actors’ value propositions; 4) Identification of interaction between actors; 5) Verification of business ecosystem architecture design. This paper uses the Danish electricity system as an example to introduce the methodology, and use Electric Vehicle home charging as a case study to demonstrate the application of the developed methodology. The case study demonstrates that the proposed methodology is a systematic approach and can be easily applied to any ecosystem architecture design with the five stages, and the designed ecosystem architecture can represent the physical system and business. Several definitions are clarified in the paper, e.g., actor, role, interaction, ecosystem roadmap and expanded/shifted ecosystem, etc. With clear definitions, the proposed methodology provides a visualized, clear structure of behaviors and specifications for a given business ecosystem.