Construction of Energy Scheduling Model for Iron and Steel Enterprises Based on Big Data

Abstract

With the development of social economy and the expansion of the scale of iron and steel enterprises, as an important symbol of national industrialization level and international competitiveness, energy management of iron and steel enterprises has gradually become the key issue of national development strategy and national long-term interests. In recent years, with the rapid development of big data technology, the application of big data technology in energy scheduling of iron and steel enterprises is more extensive. At present, most dispatchers of iron and steel enterprises estimate roughly the fluctuation of blast furnace gas production in the future based on historical data, and the accuracy of the estimation mainly depends on the experience of the dispatchers. At the same time, the existing point by point prediction model cannot meet the requirements of energy system for prediction accuracy. Through long-term field research and investigation, it is found that the dispatcher often pays more attention to the reliability of its estimation results, rather than a single estimation value. With the support of today’s big data. The decision support based on data analysis is being integrated into the energy management ideas of equipment manufacturing enterprises, which improves the level of energy management of equipment manufacturing enterprises, optimizes the industrial structure of enterprises, improves work efficiency and creates social wealth. Through literature research and other methods, and through certain experiments, it is concluded that the average energy consumption of China’s steel industry is 15% higher than that of the world-class countries; and in the cost of energy consumption, the average level of China’s enterprises is about 30%, and some enterprises even exceed 40%. It can be seen that China’s iron and steel enterprises are still struggling in energy conservation.