面向制造企业动力能源的智能供应与管控研究

doi:10.3785/j.issn.1006-754X.2024.04.115

工程设计学报

2024, Vol. 31

Issue (6): 697-706 DOI: 10.3785/j.issn.1006-754X.2024.04.115

【特约专栏】“双碳”背景下新型能源装备设计、制造、运维关键技术及其应用

面向制造企业动力能源的智能供应与管控研究

蔡霞(

),王可,郑军红(

),何利力

浙江理工大学计算机科学与技术学院（人工智能学院），浙江杭州 310018

Research on intelligent supply and management of power energy for manufacturing enterprises

Xia CAI(

),Ke WANG,Junhong ZHENG(

),Lili HE

School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, China

全文: PDF(3035 KB) HTML

摘要：

为了实现碳达峰、碳中和目标，需要持续推动制造企业高质量发展，构建节能降耗的新发展格局。针对新的发展需求，制造企业的能源结构需要不断优化，而动力能源管控方式直接影响制造企业的能源结构布局。制造企业内不同的职能车间存在不同的设备运行方式和能源消耗特性，多年生产运行积累了大量的能源使用数据，但这些数据并未被充分利用，存在数据孤岛现象，严重影响了制造企业的生产效益。因此，亟需一种针对制造企业不同工况需求的动力能源管控方法。为解决上述问题，以蒸汽能源为例，构建了工艺用蒸汽预测模型和空调用蒸汽预测模型，以实现满足制造企业生产工艺需求的蒸汽智能供应与管控。首先，提出了基于划分工段的工艺用蒸汽预测模型，用于预测具有强时段性的工艺蒸汽用量；模型改进后工艺平均能耗折标降低了5.12%。然后，构建了基于混合深度学习的组合式空调用蒸汽预测模型和基于多场景的独立式空调用蒸汽预测模型；通过与其他预测模型的对比，验证了所提出模型的有效性和准确性。结果表明，所提出的动力能源预测模型具有一定的广泛适用性，通过适当修改调整后可将其应用于其他制造企业不同动力能源的管控。研究结果有助于相关制造企业充分利用历史能源使用数据，实现动力能源的节能增效，进而为我国制造企业的数字化转型升级提供有力支持。

关键词： 动力能源; 智能管控; 蒸汽供应; 多工况预测; 混合深度学习

Abstract:

In order to achieve the goal of carbon peak and carbon neutrality, it is necessary to continuously promote the high-quality development of manufacturing enterprises and establish a new development pattern of energy saving and consumption reduction. In response to the new development needs, the energy structure of manufacturing enterprises needs to be continuously optimized, and the power energy management method directly affects the energy structure layout of manufacturing enterprises. Different functional workshops in manufacturing enterprises have different equipment operation modes and energy consumption characteristics. Many years of production and operation have accumulated a large amount of energy usage data, but these data have not been fully utilized, resulting in data isolation and seriously affecting the production efficiency of manufacturing enterprises. Therefore, there is an urgent need for a power energy management method for different working conditions of manufacturing enterprises. In order to solve the above problems, taking steam energy as an example, the steam prediction models for process and air-conditioning were constructed to realize the intelligent supply and management of steam to meet the production process requirements of manufacturing enterprises. Firstly, a steam prediction model for process based on work section division was proposed to forecast process steam consumption with strong periodic characteristics. After the model improvement, the average standard energy consumption for process was reduced by 5.12%. Then, a steam prediction model for modular air-conditioning based on hybrid deep learning and a steam prediction model for independent air-conditioning based on multiple scenarios were constructed. Through comparing with other prediction models, the effectiveness and accuracy of the proposed model were verified. The results showed that the proposed power energy prediction model had wide applicability and could be applied to the management of different power energy in other manufacturing enterprises after proper modification and adjustment. The research results are helpful for relevant manufacturing enterprises to make full use of historical energy usage data, achieve energy conservation and efficiency improvement in power energy, and provide strong support for the digital transformation and upgrading of manufacturing enterprises in China.

Key words: power energy intelligent management steam supply multi-condition prediction hybrid deep learning

收稿日期: 2024-02-26 出版日期: 2024-12-31

CLC:

TK 018

基金资助: 浙江省重点研发“领雁”计划项目(2021C01110)

通讯作者: 郑军红 E-mail: cxdaisy@zstu.edu.cn;zjhist@zstu.edu.cn

作者简介: 蔡霞（1971—），女，讲师，硕士，从事数据智能与机器学习研究，E-mail: cxdaisy@zstu.edu.cn，https://orcid.org/0009-0006-1103-3416

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引用本文:

蔡霞,王可,郑军红,何利力. 面向制造企业动力能源的智能供应与管控研究[J]. 工程设计学报, 2024, 31(6): 697-706.

Xia CAI,Ke WANG,Junhong ZHENG,Lili HE. Research on intelligent supply and management of power energy for manufacturing enterprises[J]. Chinese Journal of Engineering Design, 2024, 31(6): 697-706.

链接本文:

https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2024.04.115 或 https://www.zjujournals.com/gcsjxb/CN/Y2024/V31/I6/697

图1 LSTM网络结构

图2 工单启停时段蒸汽用量预测流程

图3 工单运行稳定时段蒸汽用量预测模型的整体架构

图4 PredRNN++整体架构

图5 组合式空调用蒸汽预测模型的整体架构

图6 存储隔断区域独立式空调用蒸汽预测流程

图7 生产隔断区域独立式空调用蒸汽预测模型的整体架构

表1 工单时段划分结果

图8 各工段工艺蒸汽用量的预测值与实际值对比

图9 基于不同模型的组合式空调蒸汽用量预测结果对比

表2 组合式空调用蒸汽预测模型的性能对比

表3 独立式空调用蒸汽预测模型的性能对比

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