The role of knowledge engineering in enhancing renewable energy systems for industrial applications

Transitioning to renewable energy systems (RES) is crucial for reducing industrial carbon emissions and meeting decarbonization targets. While Knowledge Engineering (KE) proves effective in traditional energy systems, its application in optimizing RES in industrial contexts remains underexplored. Existing research lacks comprehensive methodologies for integrating KE with RES to address their inherent variability and unpredictability. This study adopts a quantitative approach, utilizing survey-based data collection and secondary data analysis. KE tools used in industrial RES are classified based on reasoning type, data handling capabilities, and application environments. A sample of at least 100 professionals from the renewable energy sector is surveyed, and statistical techniques, including ANOVA, Chi-square tests, and machine learning models such as decision trees and random forests, are applied to analyze the data. These models are rigorously evaluated using nested cross-validation and multiple performance metrics (accuracy, ROC–AUC, PR–AUC, F1, MCC) to ensure robustness and reliability of findings. When analyzed by reasoning type, Hybrid reasoning tools (n = 18) achieve a mean effectiveness rating of 4.2 in fault detection, significantly outperforming Object-oriented reasoning (n = 15, mean = 3.7) by approximately 15 %. In contrast, pooled results across all KE tools yield lower overall fault detection effectiveness (mean ≈ 2.9–3.0), underscoring the importance of subgroup-specific analysis. Object-oriented reasoning also shows superior performance in energy optimization, achieving a mean rating of 3.8. This research offers novel insights into integrating KE tools to enhance RES in industrial environments. This study provides a novel classification and performance analysis of KE tools. It enhances RES deployment and operational efficiency in industrial settings, directly contributing to sustainable energy access (SDG 7) and climate mitigation (SDG 13).