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.

Forecasting CNY exchange rate accurately is a challenging task due to its complex coupling nature, which includes market-level coupling from interactions with multiple financial markets, macro-level coupling from interactions with economic fundamentals and deep coupling from interactions of the two aforementioned kinds of couplings. This study develops a new deep coupled Long Short-Term Memory (LSTM) approach, namely DC-LSTM, to capture the complex couplings for USD/CNY exchange rate forecasting. In this approach, a deep structure consisting of stacked LSTMs is built to model the complex couplings. The experimental results with 10 years data indicate that the proposed approach significantly outperforms seven other benchmarks. The DC-LSTM is verified to be a useful tool to make wise investment decisions through a profitability discussion. The purpose in this paper is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings.

Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.

As wind and solar power displace conventional generation in the electricity grid, there is an urgent need for technologies that can deal with the variation in supply. Demand response technology has been proposed as a solution to make the demand-side flexible and able to effectively adjust to variations in supply. However, consumers do not simply invest in demand response technologies without insurance that their investment will pay back. This paper applies agent-based simulation to investigate consumer adoption behaviour of implicit demand response solutions that allow consumers to adjust their electricity use to the hourly prices in day-ahead spot markets. The simulation uses the case study of a water supply system to show that the adoption speed of implicit demand response technology depends on both technical characteristics of the system and the business model of the implicit demand response solution provider. Hence, this paper contributes with insight on how agent-based simulation can help technology providers to design solutions which match the needs of system operators.

Agent-based simulation of the decision-making process for adoption of smart energy solutions can provide evidence for smart energy solutions' providers to decide for a business strategy which results in the best adoption rate. The adoption rate of smart energy solutions is important in achieving climate goals, such as the Danish goal to have 100% renewable electricity production by 2030. This paper shows how agent-based simulation can be used to investigate the decision-making process for adoption of smart energy solutions. The study investigates a case about Danish commercial greenhouse growers' adoption of a demand response program. The simulation outputs an adoption curve and grower information. The results provide the maximum monetary cost for achieving an adoption rate of 50% in 5 years.

Intelligent Tutoring Systems are increasingly used in higher education to support personalized learning and academic monitoring in large-scale digital environments. However, existing systems are predominantly based on static architecture and rigid rule-based mechanisms, which limit scalability and hinder effective adaptation to heterogeneous learners, evolving learning behaviors, and real-world educational contexts. This paper presents a self-adaptive multi-agent architecture based on Reinforcement Learning for autonomous decision-making in intelligent systems deployed in real environments. The proposal integrates an RL Meta-Agent that dynamically optimizes the selection of specialized agents through an intelligent switching mechanism, considering the user’s state, behavior, and interaction patterns. The architecture was implemented in Moodle using flows orchestrated in n8n, LLMs, databases, APIs developed in Django, and real academic data. For the empirical evaluation, a real and a simulated case study were designed. A questionnaire was administered to university students, considering dimensions of usability, satisfaction and usefulness, and accessibility and interaction, to understand the perception of the system and improvements. The quantitative data were analyzed using descriptive statistics and nonparametric tests (Mann–Whitney U and Kruskal–Wallis), while the qualitative data were examined using thematic categorization. A simulated case study was conducted to analyze the behavior of the system. The results show that the RL Meta-Agent significantly improves system efficiency, response relevance, and adaptive agent selection, demonstrating that self-adaptive RL-based MAS architectures are a viable solution for intelligent systems applied in real-world contexts, providing empirical evidence of their performance and adaptability in complex scenarios such as higher education.