Conclusion

This study explored the feasibility of a self-sufficient offshore island resort through an integrated systems engineering approach that links energy infrastructure, spatial configuration, and tourism facilities within a unified system boundary. By combining ontology-based knowledge representation with parametric modeling, the project established both a conceptual and quantitative framework for evaluating key performance criteria, including visitor accommodation capacity, power supply reliability, and environmental impact. The results highlight that island development is governed primarily by system-level interdependencies rather than isolated component performance: building scale directly shapes energy demand, turbine placement affects transmission efficiency, and spatial decisions influence ecological preservation. The comparison of design alternatives demonstrates that extreme strategies tend to amplify trade-offs, whereas the integrated optimal solution achieves a balanced outcome with stable capacity, positive energy surplus, and minimal environmental footprint. Overall, the research illustrates the value of coupling semantic system modeling with parametric simulation to support early-stage decision-making and provides a transferable methodology for sustainable infrastructure planning in constrained, off-grid environments.

Limitation and Future Work

Several limitations should be acknowledged. The study focuses on the operational phase and does not incorporate lifecycle factors such as construction complexity, transportation, or economic cost emphasizing simplified assumptions for wind generation and building energy demand rather than time-dependent or stochastic conditions. In addition, the absence of energy storage and hybrid renewable sources limits the realism of the island microgrid, while the fixed onshore layout restricts exploration of alternative spatial configurations that could further optimize system performance. The ontology currently functions as a conceptual decision-support structure and has not yet been coupled with automated optimization tools. Future research should therefore integrate lifecycle analysis, dynamic energy modeling, storage technologies, and more flexible spatial parameters to enhance predictive accuracy and move the framework closer to practical engineering application.

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