# Development of an Intelligent Solution for the Optimization of Hybrid Energy Systems
###### Abstract  
This paper presents a proposal for the development of a new intelligent solution for the optimization of hybrid energy systems. This solution is of great importance for installers of hybrid energy systems, as it helps them obtain the best configuration of the hybrid energy system (efficient and less expensive). In this solution, it is sufficient to enter the name of the location of the hybrid energy system that we want to install; after that, the solution will show the name of the best technology from which the optimal configuration of this system can be obtained. To accomplish this goal, the study relied on the ontology approach for two reasons, one of which is related to the nature of hybrid systems, because it is characterized by a large amount of information that requires good structuring, and the second reason is the interaction of hybrid energy systems with the external environment (climate, site characteristics). Afterward, to develop the knowledge base of the ontology, many steps were followed, the first of which is related to a detailed study of the existing one and the extraction of the basic elements, such as the concepts and the relations between them, followed by the development of the rules of intelligent reasoning, which is an interaction between the elements of the ontology through which all possible cases are treated. The \"Protege\" software was used to edit these elements and perform the simulation process to show the results of the developed solution. Finally, the paper includes a case study, and the results show the importance of the developed solution, and it is open to future developments. decision-making tool; intelligent reasoning rules; energy saving; energy domain ontology; hybrid energy system +
Footnote †: journal: Applied Sciences  
costs and reliability [3]. Therefore, it is necessary to provide new solutions and focus in particular on meeting consumer demands for energy while ensuring the minimum cost [4]. To meet the rapidly increasing demand for energy, all energy sources must be exploited. Renewable energies are unlimited and clean, but the biggest problem with them lies in their intermittent aspect. To overcome this problem, a combination of several energy sources is made to obtain the so-called hybrid renewable energy system [5; 6]. Many works have been carried out on HES optimization methodologies. A study by [PERSON] et al. aimed at presenting and analyzing a literature review of recently published works in the field of hybrid renewable energy [7]. The study focuses on four basic categories of hybrid renewable energy systems, which are scaling (using software or using conventional methods), optimization (classical, synthetic, and hybrid methods), control (centralized, distributed, and hybrid control), and energy management (technical and economic objective). Furthermore, the review compares the different methods used in each category. A research work by [PERSON] et al. presents a hybrid approach to process improvement and addresses uncertainties in a residential energy system consisting of photovoltaics, fuel cells, boilers, and storage units [8]. In this regard, uncertain parameters are divided into two categories, including poorly behaved and well-behaved parameters, and then robust optimization and stochastic programming are used for modeling. Additionally, the conditional value at risk is implemented to assess the risks of well-behaved parameters. According to simulations, it has been shown that uncertainties with bad behavior have greater effects on the operation of the system. Moreover, changing the control parameters for robust optimization and conditional value at risk from 0 to 24 and from 0 to 1 increases the total cost by 5.2% and 0.47%, respectively. Comparative results also show that the proposed hybrid method makes less conservative decisions for cost optimization. The application of hybrid energy storage to distributed energy systems can greatly improve energy efficiency and reduce operating costs.