TAXONOMY OF UNCERTAINTY FACTORS IN HOUSEHOLD ENERGY SYSTEMS BASED ON THE DIGITAL TWIN CONCEPT

Abstract

This paper substantiates a systematized taxonomy of uncertainty factors within domestic energy systems in the context of Digital Twin (DT) technology implementation. The relevance of this study is driven by the urgent necessity to enhance energy efficiency in the residential sector and integrate renewable energy sources amidst stochastic processes. Based on a critical analysis of specialized literature, four fundamental dimensions of uncertainty are identified and categorized: behavioral, technical, generational, and market-related.
The behavioral dimension encompasses the unpredictability of human consumption patterns, while the technical dimension focuses on measurement errors and hardware degradation. The generational dimension analyzes the volatility of renewable energy sources, and the market dimension addresses complex tariff dynamics. For each dimension, the study defines primary sources, quantitative impact indicators, and robust mitigation methods through algorithmic adaptation. The research methodology is based on a synergistic combination of mathematical modeling and advanced data mining techniques, allowing for the significant minimization of random factors' influence on forecast accuracy.
The original contribution of this paper is the impact matrix of each uncertainty type on the core functions of a digital twin. Specifically, the correlation of factors with Non-Intrusive Load Monitoring (NILM) processes is investigated, enabling consumption decomposition without installing additional hardware sensors. Particular attention is paid to local generation forecasting and the intelligent optimization of current energy flows in real-time.
Two systemic chains of interdependence among uncertainty factors have been identified, which are of fundamental importance for the design of adaptive Home Energy Management Systems (HEMS). These chains demonstrate how errors in individual data segments cumulatively affect overall model stability. The results of the study form a comprehensive theoretical framework for developing hybrid methods for constructing household digital twins. The proposed approaches create a solid foundation for the implementation of predictive maintenance and increasing the operational autonomy of energy systems within the Smart Grid structure.