关键词: 能源监控, 大数据技术, 纺纱车间, LoRa, VMD, LSTM

Abstract: Against the backdrop of the accelerating global industrialization process and the increasingly scarce energy resources, the textile industry is undergoing a critical transformation phase. Driven by policy guidance and market competition, the textile industry is moving towards digitalization, intelligence and greening. However, many traditional spinning enterprises still rely on manual meter reading and analyzing and calculating energy consumption data. This method is not only inefficient but also prone to errors, making it difficult to accurately grasp workshop energy consumption and restricting the energy management capabilities and production efficiency of these enterprises.
To solve this problem, this paper proposes an energy monitoring platform for spinning workshops based on Internet of Things (IoT) and big data technology. The platform establishes an energy data IoT monitoring network through smart meters equipped with LoRa wireless transmission technology, which realizes the comprehensive and timely collection of energy data. Big data technologies, including HDFS, HBase, Spark and other components in the Hadoop ecosystem, are used to provide a multi-tiered storage and analysis architecture for energy consumption data, which ensures fast integration, efficient computation and flexible querying of data. A multi-step energy consumption prediction model based on variational modal decomposition (VMD), particle swarm optimization (PSO), and long-short-term memory neural network (LSTM) is proposed, and the power consumption data and relevant operational data of a spinning enterprise in Xinjiang were selected for verification is validated. The experimental results show that the model outperforms traditional algorithms such as BP, LSTM, PSO-LSTM, and VMD-LSTM in single-step, three-step, and six-step energy consumption prediction. Specifically, its single-step average prediction error is reduced by 74.50% compared to LSTM and 39.07% compared to VMD-LSTM, and the R² values for single-step, three-step, and six-step predictions are 0.9820, 0.9642, and 0.9151, respectively, demonstrating high prediction accuracy. Finally, based on the B/S architecture, this paper has developed a Web module, integrating functions such as real-time monitoring of energy consumption data, cluster management, and energy consumption prediction. Users can intuitively view energy consumption data, perform energy consumption predictions, and manage energy usage through a user-friendly interface.
In summary, the energy monitoring platform for spinning workshops proposed in this paper integrates the IoT technology, big data technology and software development technology, which provides a more intelligent solution for the energy management of traditional spinning enterprises, and a solid data basis for enterprise energy optimization decision-making, helping them to improve energy efficiency, reduce production costs, and enhance their competitiveness in the fierce market competition. It also serves as reference for the traditional textile industry's low-carbon emissions reduction, sustainable development, as well as digital and intelligent transformation.

Key words: energy monitoring, big data technology, spinning workshops, LoRa, VMD, LSTM