Обґрунтування складових цифровізованого інструментарію контролю енергоспоживання підрядними підприємствами

This article addresses the issue of managing energy efficiency in construction production for contracting enterprises, which arises due to the gaps between periods of energy resource consumption and the inflow of financial resources for their payment. It has been determined that traditional methods of assessing payment capacity deficits do not provide a well-founded answer regarding the financial and energy security of construction enterprises. Given this, the article proposes conceptual foundations for designing intelligent energy consumption control systems for contracting enterprises, considering the formation of financial capabilities to cover expenditures on energy carriers and electricity. Based on cost-estimating norms in construction, indicator variables have been justified, the variation of which serves as a basis for strengthening energy consumption control. Threshold values have been determined, where approaching them indicates the advisability or inadvisability of revising the energy intensity policy of construction production and its organization. The dynamics of resource consumption have been studied in accordance with the substantiated technological methods of performing construction works. It has been found that energy consumption growth is not uniform but depends on partial increases in the estimated cost of materials, construction, and the normatively calculated labor intensity. The practical feasibility of applying quantitative indicator values using traditional mathematical statistics methods, particularly k-means clustering, has been proven. A detailed argument is provided in favor of using fuzzy set theory methods for energy consumption management in construction, specifically: membership functions, their types, quantity, and parameters; logical connections and structures; as well as the application of fuzzy logical statements. The results of research aimed at justifying the components of a digitalized control toolkit are summarized in a comprehensive five-stage algorithm suitable for use by economic department specialists or management personnel without specialized IT project design education. At the same time, the obtained results hold significant scientific and practical potential for the development of artificial intelligence systems capable of managing energy consumption in contracting enterprises based on fuzzy logic and machine learning.