Methods for improving the operational quality and reliability control of energy-consumptive buildings using intellectual data analysis

Abstract

The article explores the possibilities of using intelligent data analysis technologies (Data Mining) to assess the operational efficiency of modern buildings characterized by high energy consumption and information saturation. A typical set of DM methods is considered, their advantages and limitations are classified, and the criteria for choosing algorithms depending on the analysis objectives are substantiated. Particular attention is paid to the role of large volumes of operational data as a source for identifying hidden patterns in the operation of engineering systems and finding opportunities to improve the energy efficiency of buildings.

It is emphasized that traditional methods of technical supervision and assessment of the condition of buildings do not always provide for the prompt detection of potentially dangerous defects or irrational energy consumption. The complexity of diagnostics is due to the variability of design solutions, the influence of external factors and the complexity of interpreting data without appropriate digital tools.

In this context, the need to implement modern instrumental reliability control systems capable of functioning at all stages of the building life cycle - from commissioning to maintenance and reconstruction is substantiated. The combination of continuous monitoring of structural elements with methods of intelligent data analysis makes it possible to significantly increase the level of safety and manageability of objects, reduce maintenance costs, and extend the period of their reliable operation.