Algorithm and Methods for Analyzing Power Consumption Behavior of Industrial Enterprises Considering Process Characteristics

Power consumption management is crucial to maintaining the reliable operation of power grids, especially in the context of the decarbonization of the electric power industry. Managing power consumption of industrial enterprises by personnel proved ineffective, which required the development and implementation of automatic energy consumption management systems. Optimization of power consumption behavior requires comprehensive and reliable information on the parameters of the technological processes of an industrial enterprise. The paper explores the specific features of non-stationary conditions of output production and assesses the potential for power consumption management under these conditions. The analysis of power consumption modes was carried out based on the consideration of random factors determined by both internal and external circumstances, subject to the fulfillment of the production plan. This made it possible to increase the efficiency of power consumption in mechanical engineering production by taking into account the uncertainty of seasonal and technological fluctuations by 15–20%, subject to the fulfillment of the production plan. This study presents a justification for utilizing the theory of level-crossings of random processes to enhance the reliability of input information. The need to analyze the specific features of technological processes based on the probabilistic structure and random functions is proven. This is justified because it becomes possible to fulfill the production plan with technological fluctuations in productivity and, accordingly, power consumption, which exceeds the nominal values by more than 5%. In addition, the emission characteristics are clear, easy to measure, and allow the transition from analog to digital information presentation. The algorithm and methods developed to analyze the power consumption patterns of industrial enterprises can be used to develop automatic power consumption management systems.