Application of ontological modeling for automatic selection of significant features and semantic regularization of machine learning models for the development of intelligent information systems in the power industry
Abstract
Application of ontological modeling for automatic selection of significant features and semantic regularization of machine learning models for the development of intelligent information systems in the power industry
Incoming article date: 09.07.2025Ontological modeling is a promising direction in the development of the scientific and methodological base for developing intelligent information systems in the power industry. The article proposes a new approach to using ontological models in creating artificial intelligence systems for forecasting time series in electrical engineering problems. Formal metrics are introduced: the ontological distance between a feature and a target variable, as well as the semantic relevance of a feature. Using examples of domain ontologies for wind energy and electricity consumption of an industrial enterprise, algorithms for calculating these metrics are demonstrated and it is shown how they allow ranking features, implementing an automated selection of the most significant features, and providing semantic regularization of training regression models of various types. Recommendations are given for choosing coefficients for calculating metrics, an analysis of the theoretical properties of metrics is carried out, and the applicability limits of the proposed approach are outlined. The results obtained form the basis for further integration of ontological information into mathematical and computer models for forecasting electricity generation and consumption in the development of industry intelligent systems.
Keywords: ontology, ontological distance, feature relevance, systems analysis, explainable artificial intelligence, power industry, generation forecasting, electricity consumption forecasting