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Keywords
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Energy Hub, Large Language Model (LLM), Optimization, Pareto Front, Fuzzy Satisfaction
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Abstract
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Energy hub optimization depends on compromises between multiple conflicting objectives. The result of this process is not a unique solution, but a set of optimal solutions constituting the Pareto front. The next step is to choose the best solution from the available optimal points. The most effective approach can differ according to the conditions and preferences. In this paper, a large language model (LLM) is used to select the most efficient approach in the Pareto front. LLMs, due to their ability to understand human language, can take into account existing conditions and preferences. This capability llows
LLMs to be employed in decision-making to select the best approach without the need for mathematical or formulaic complexities. To evaluate the performance of the LLM, an energy hub was optimized, and its Pareto front was provided to
the model. The LLM selected the best solution based on user preferences and conditions. The results were compared with the fuzzy satisfaction method, demonstrating flexible and effective decision-making
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