Datasets in the energy domain often encounter significant challenges in achieving data interoperability due to semantically equivalent concepts being inconsistently named. Semantic annotation addresses this issue by standardizing concepts to enhance data interoperability and facilitate efficient data integration. Traditional annotation methods, however, rely heavily on static resources, which limit their ability to dynamically understand cross-domain context. Large Language Models (LLMs) have shown promise in capturing semantic similarities between terms, and their capabilities can be enhanced by incorporating ontological reasoning. In this paper, we propose a novel framework that leverages LLMs for ensemble decision-making to automate and improve semantic annotation. By combining LLM-based semantic understanding with structural reasoning from ontologies, our approach enhances reliability and annotation performance. We evaluate its effectiveness using both proprietary and open-source models on real data from industrial companies in the energy sector. The experimental results demonstrate superior annotation accuracy compared to baseline methods. This study highlights the potential of integrating ontological reasoning, LLMs, and ensemble decision-making to advance semantic data integration in the energy domain.