Knowledge Engineering with Large Language Models: Accelerating Fuzzy Rule Bases Development for Energy-Aware Expert Systems

Expert systems offer a promising way to automatically identify energy efficiency potentials in industry and thereby contribute to energy cost savings and decarbonization. In these systems, domain-specific knowledge is embedded and linked to automated analyses of measurement data. Until now, knowledge engineers have extracted, structured, and represented the necessary domain-specific knowledge in a form usable by expert systems, which is time-consuming and costly. This article presents a hybrid approach that couples expert systems with large language models to support the work of knowledge engineers. Energy performance indicators, selected by the energy manager, serve to quantify changes in energy performance and reproduce the heuristic decision-making of human experts on a quantitative basis. These indicators then form the basis for a rule set that targets areas with the highest potential energy savings. For practical implementation, a fuzzy rule base is applied because it captures decisions made under imprecise information and allows conditions and conclusions that can be partially true or false. Building the fuzzy rule base involves assigning membership functions to input and output variables and defining their linguistic partitioning, since these choices shape both sensitivity and interpretability. The rule base is implemented as generally understandable IF–THEN rules. The premise consists of energy performance indicators that are associated with linguistic variables and combined using logical operators. The conclusion contains priority numbers, which are also associated with linguistic variables and express the energy efficiency potential. In the hybrid setup presented in this article, large language models formalize given energy performance indicators and fuzzy rules, propose membership functions to populate the fuzzy rule base, and generate visualization scripts in Python. This leads to accelerated development while preserving transparent, comprehensible, and reproducible decision logic characteristic of expert systems. The approach is demonstrated using a foam panel production line in the chemical industry.