A Big Data Methodology for Analyzing Machine Energy Consumption Based on IIoT Technology under ESG Concept

The development scenario of “green credit” has prompted many investment institutions to pay more attention to the “green performance” of enterprises than the conventional financial performance. In recent years, the number of companies issuing sustainable development reports and ESG (environmental, social and Governance) performance data has significantly increased. Meanwhile, with the high digital computing information development in modern world, data type and amount in many fields is growing in an astonishing speed, which leads to the appearance of the era of “industrial Internet of Things” (IIoT). Due to the characteristics of its volume, variety and velocity, making it difficult for the traditional computing infrastructure to handle Big Data. The amount of energy consumption on shop floors by CNC milling machines has come under increasing scrutiny lately. To fulfill the rising need for more energy-efficient and environmentally friendly machining techniques in manufacturing settings. The theoretical approaches we had at the time, however, couldn’t manage production issues adequately when they were unknown since the conventional manner of manufacturing lacked a mechanism to get real-time information on energy. In order to make machine milling more sustainable and energy-efficient, a Wireless Sensor Networks system based on Hadoop has been developed in this study. It tracks energy usage in real time. obtaining the most recent data from three distinct milling machines in a business via the Hive HiveQL language. Examine the daily energy consumption of every machine and compare the weekly energy consumption of three different machines. The results showed that the energy consumptions in the machine standby and switch off mode should be decreased in order to save energy.