陈雪斌, 袁逸萍, 财音宝音, 朱广贺. 面向铝箔车间综合能耗预测系统的研发与应用[J]. 制造技术与机床, 2023, (9): 80-87. DOI: 10.19287/j.mtmt.1005-2402.2023.09.011
引用本文: 陈雪斌, 袁逸萍, 财音宝音, 朱广贺. 面向铝箔车间综合能耗预测系统的研发与应用[J]. 制造技术与机床, 2023, (9): 80-87. DOI: 10.19287/j.mtmt.1005-2402.2023.09.011
CHEN Xuebin, YUAN Yiping, CAIYIN Baoyin, ZHU Guanghe. Development and application of a comprehensive energy consumption prediction system for aluminum foil workshops[J]. Manufacturing Technology & Machine Tool, 2023, (9): 80-87. DOI: 10.19287/j.mtmt.1005-2402.2023.09.011
Citation: CHEN Xuebin, YUAN Yiping, CAIYIN Baoyin, ZHU Guanghe. Development and application of a comprehensive energy consumption prediction system for aluminum foil workshops[J]. Manufacturing Technology & Machine Tool, 2023, (9): 80-87. DOI: 10.19287/j.mtmt.1005-2402.2023.09.011

面向铝箔车间综合能耗预测系统的研发与应用

Development and application of a comprehensive energy consumption prediction system for aluminum foil workshops

  • 摘要: 针对高能耗企业能源消耗管理问题,设计了一种基于Lora传输的企业能耗监测与预测系统。系统由软、硬件两方面组成。硬件方面主要有智能仪表、Lora模块和LoraWAN网关,智能仪表进行采集能源消耗,Lora模块对采集数据进行接收与传输,同时采用以LoraWAN网关为中心节点的星形拓扑网络对平台服务器进行映射;软件方面采用B/S架构,整体以SpringBoot、Vue2作为主框架,Mybatis-plus作为ORM框架,通过Shiro进行权限控制。针对车间产生的大量能耗数据,设计了一种基于APSO-LSTM算法的预测模型对车间能耗值进行预测,进而为企业实现节约成本和低碳生产提供参考。测试结果表明,系统可视化效果良好,能耗预测准确性较高,有一定的应用价值。

     

    Abstract: An energy consumption monitoring and prediction system based on Lora transmission is designed for the energy consumption management of high energy consumption enterprises. The system as a whole is divided into two pieces: software and hardware. The hardware mainly consists of smart meter, Lora module and LoraWAN gateway. The smart meter collects the main energy consumption of the enterprise, and the Lora module accepts and transmits the collected data. Also, the platform server is mapped using a star topology network with the Lora-WAN gateway as the central node. The software uses a B/S architecture that is more suitable for enterprises. The landing system as a whole uses SpringBoot and Vue2 as the main framework, with Mybatis plus as the ORM framework, and uses Shiro for access control. The database selected for the system is MYSQL. In addition, a prediction model based on the APSO-LSTM algorithm was designed to predict the energy consumption value of the workshop for the large amount of energy consumption data generated in the workshop, which can provide a reference for enterprises to achieve cost saving and low carbon production. The test results show that the designed system has good visualization effects and high accuracy of energy consumption prediction, which has certain application value.

     

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