This study investigates the use of Singular value decomposition to decompose time series of electricity consumption from substation feeders. The goal is to identify and classify the electrical load patterns of mining enterprises. The need for continuous improvement in process efficiency is dictated by current trends and tendencies towards increased consumption of fossil fuels and energy resources. The proposed algorithm uses the decomposition results to identify similarities in consumption patterns, enabling the categorization of loads into broader groups. Based on the results of the analysis of electricity consumption data for two independent feeders, the formation of similar recurring characteristic load changes (temporal patterns) with a period of three days was identified. The results facilitate the automated typification and classification of load profiles. This is vital for integrating economic incentives into demand management and for assessing the feasibility and potential of consumer participation in load schedule regulation via demand side management technologies. The proposed algorithms enable the use of these typical consumption profiles to calculate quasi-dynamic electrical modes, supporting tasks related to the long-term development of energy supply systems and energy efficiency improvements for mining enterprises.