The average temperature of the earth's surface has become higher and higher over the years, and carbon emissions are an important driving force. Many countries around the world, especially the western developed countries, pay more and more attention to this. After China's entry into industrialization, energy consumption such as coal and electricity has increased year by year, and carbon emissions have also increased. In terms of carbon emissions, China is facing a very severe international situation. With the rapid development of information technology, people realize that the informatization of energy management is an important way to reduce carbon emissions. Building carbon emissions account for a large proportion of China's total carbon emissions. As a traditional high energy consuming enterprise, the steel industry has a large amount of carbon emissions, coupled with fierce market competition. Therefore, energy conservation and emission reduction, and the construction of energy management system are very important topics for steel enterprises. Based on the actual production data of steel enterprises, the energy management system unifies the energy-saving performance measurement methods. Through the joint efforts of all departments, the closed-loop management of energy can be achieved, which has a very significant effect on the energy conservation and consumption reduction of steel enterprises and the improvement of energy management level. By studying the energy consumption composition and main energy consumption indicators of steel enterprises, this paper establishes an energy management system suitable for steel enterprises based on big data(BD), Internet of things and cloud computing technology. Finally, the huge benefits obtained after the implementation of the energy management system are analyzed, combined with the current development of BD analysis technology and intelligent optimization control technology and the possible future development direction of the energy management system.

Jin Zhao. Energy Management Business in the Context of Big Data. Academic Journal of Energy (2022), Vol. 3, Issue 2: 10-19. https://doi.org/10.38007/RE.2022.030202.

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