Achieving Interoperability in Energy Systems through Multi-Agent Systems and Semantic Web

Energy systems are composed of interacting components and subsystems, commonly owned by different actors and stakeholders. For several decades, multi-agent systems (MAS) have been used to effectively model and manage energy systems. However, interactions between heterogeneous constituents of such systems remain one of the key challenges. In this paper, we present an approach that enables such components to interoperate seamlessly. The approach is implemented based on SPARQL-Act, a SPARQL-based agent communication language (ACL), to provide a platform-independent ACL. It also manages agents' knowledge bases and handles messages of different performatives. The use of RDF as the content language of exchanged messages and ontologies contributes to the interoperability of agent interactions. The approach was validated in a use case of building energy management of CityLearn. We modelled and implemented a rule-based MAS for optimising the Heating, Ventilation, and Air-Conditioning (HVAC) system of a building. The results show that our proposed solution allows agents to communicate without ambiguity, to manage their knowledge bases, and to interact effectively to achieve their individual as well as the system's objectives.