Please wait a minute...
工程设计学报
Chinese Journal of Engineering Design  2024, Vol. 31 Issue (6): 697-706    DOI: 10.3785/j.issn.1006-754X.2024.04.115
【Special Column】Key Technologies of Design, manufacture, operation and maintenance for New Energy Equipment and Their Applications under the Carbon Peaking and Carbon Neutrality Goals     
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
Download: HTML     PDF(3035KB)
Export: BibTeX | EndNote (RIS)      

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     
Received: 26 February 2024      Published: 31 December 2024
CLC:  TK 018  
Corresponding Authors: Junhong ZHENG     E-mail: cxdaisy@zstu.edu.cn;zjhist@zstu.edu.cn
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Xia CAI
Ke WANG
Junhong ZHENG
Lili HE
Cite this article:

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

URL:

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


面向制造企业动力能源的智能供应与管控研究

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 
Fig.1 LSTM network structure
Fig.2 Prediction process of steam consumption during work order start and stop periods
Fig.3 Overall architecture of steam consumption prediction model during stable work order operation period
Fig.4 Overall architecture of PredRNN++
Fig.5 Overall architecture of steam prediction model for modular air-conditioning
Fig.6 Steam prediction process for independent air-conditioning in storage partition area
Fig.7 Overall architecture of steam prediction model for independent air-conditioning in production partition area

工单

编号

工单启停时段

工单运行

稳定时段

非工单

运行时段

1

07:00—07:20

09:47—10:00

07:20—09:4710:00—10:08
2

10:08—10:24

13:38—13:50

10:24—13:3813:50—14:09
3

14:09—14:22

17:51—18:02

14:22—17:5118:02—18:12
4

18:12—18:27

20:50—21:01

18:27—20:5021:01—21:12
5

21:12—21:29

23:43—23:58

21:29—23:4323:43—07:00
Table 1 Work order time division results
Fig.8 Comparison between predicted and actual process steam consumption for each work section
Fig.9 Comparison of steam consumption prediction results of modular air-conditioning based on different models
预测模型RMSE/kgMAE/kgMAPE/%
本文模型20.7516.531.97
GRU模型97.8571.258.36
LSTM网络模型62.8146.465.56
Table 2 Comparison of performance of steam prediction models for modular air-conditioning
预测模型RMSE/kgMAE/kgMAPE/%运行时间/s
本文模型102.174.85.825
SVM模型274.2173.213.340
LightGBM模型401.9299.833.723
多元线性回归模型523.5450.847.445
Table 3 Comparison of performance of steam prediction models for independent air-conditioning
[1]   国务院. 国务院关于印发“十四五”节能减排综合工作方案的通知［J］. 中华人民共和国国务院公报， 2022（5）： 46-52.
The State Council. Notice of the State Council on the issuance of the “14th Five-Year Plan” for comprehensive energy conservation and emission reduction［J］. Gazette of the State Council of the People's Republic of China， 2022（5）： 46-52.
[2]   国务院. 国务院关于促进国家高新技术产业开发区高质量发展的若干意见［J］. 中华人民共和国国务院公报，2020（21）： 9-12.
The State Council. Several opinions of the State Council on promoting high-quality development of national high-tech industrial development zones［J］. Gazette of the State Council of the People's Republic of China， 2020（21）： 9-12.
[3]   张毅， 李文强， 李彦， 等. 基于碳足迹信息模型的产品低碳创新设计［J］. 工程设计学报， 2017， 24（2）： 141-148.
ZHANG Y， LI W Q， LI Y， et al. Product low-carbon innovative design based on the carbon footprint information model［J］. Chinese Journal of Engineering Design， 2017， 24（2）： 141-148.
[4]   TRIVEDI V， BHATT N. A review on energy conservation in manufacturing industry［C］//2018 International Conference on Current Trends towards Converging Technologies. Coimbatore， Mar. 1-3， 2018.
[5]   ZHOU A H， LI J. Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China's provincial-level data［J］. Energy， 2021， 228： 120562.
[6]   张琦， 刘帅， 徐化岩， 等. 钢铁企业智慧能源管控系统开发与实践［J］. 钢铁， 2019， 54（10）： 125-133.
ZHANG Q， LIU S， XU H Y， et al. Development and practice of smart energy management and control system in iron and steel works［J］. Iron & Steel， 2019， 54（10）： 125-133.
[7]   TSAI K L， LEU F Y， YOU I. Residence energy control system based on wireless smart socket and IoT［J］. IEEE Access， 2016， 4： 2885-2894.
[8]   KHAN M， SILVA B N， HAN K. Internet of Things based energy aware smart home control system［J］. IEEE Access， 2016， 4： 7556-7566.
[9]   刘亚东. 造纸厂蒸汽消耗需求预测研究及预测系统开发［D］. 广州： 华南理工大学， 2013.
LIU Y D. Study on forecast of steam consumption demand in paper mill and development of the forecast system［D］. Guangzhou： South China University of Technology， 2013.
[10]   MOTTAHEDI M， MOHAMMADPOUR A， AMIRI S S， et al. Multi-linear regression models to predict the annual energy consumption of an office building with different shapes［J］. Procedia Engineering， 2015， 118： 622-629.
[11]   罗凤章， 张旭， 杨欣， 等. 基于深度学习的综合能源配电系统负荷分析预测［J］. 高电压技术， 2021， 47（1）： 23-32.
LUO F Z， ZHANG X， YANG X， et al. Load analysis and prediction of integrated energy distribution system based on deep learning［J］. High Voltage Engineering， 2021， 47（1）： 23-32.
[12]   MOGHADASI M， OZGOLI H ALI， FARHANI F. Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches［J］. International Journal of Energy Research， 2021， 45（1）： 879-893.
[13]   ZHOU C G， FANG Z S， XU X N， et al. Using long short-term memory networks to predict energy consumption of air-conditioning systems［J］. Sustainable Cities and Society， 2020， 55： 102000.
[14]   吕忠麟， 顾洁， 孟璐. 基于耦合特征与多任务学习的综合能源系统短期负荷预测［J］. 电力系统自动化， 2022， 46（11）： 58-66.
LÜ Z L， GU J， MENG L. Short-term load forecasting for integrated energy system based on coupling features and multi-task learning［J］. Automation of Electric Power Systems， 2022， 46（11）： 58-66.
[15]   周璇， 林家泉. 基于改进长短时记忆网络的地面空调能耗预测［J］. 北京航空航天大学学报， 2023， 49（10）： 2750-2760.
ZHOU X， LIN J Q. Prediction of ground air conditioner energy consumption based on improved long short-term memory neural network［J］. Journal of Beijing University of Aeronautics and Astronautics， 2023， 49（10）： 2750-2760.
[16]   LI X， LIU S， ZHAO L， et al. An integrated building energy performance evaluation method： from parametric modeling to GA-NN based energy consumption prediction modeling［J］. Journal of Building Engineering， 2022， 45： 103571.
[17]   WANG X W， LI Z J， ZHANG Y L. Model for predicting the operating temperature of stratospheric airship solar cells with a support vector machine［J］. Energies， 2021， 14（5）： 1228.
[18]   周皓， 刘尚林， 杨凯弘， 等. 基于互信息与支持向量回归的盾构掘进载荷预测方法研究［J］. 工程设计学报， 2022， 29（3）： 286-292.
ZHOU H， LIU S L， YANG K H， et al. Research on prediction method of driving load of shield machine based on mutual information and support vector regression［J］. Chinese Journal of Engineering Design， 2022， 29（3）： 286-292.
[19]   毛君， 李强， 谢苗， 等. 多目标优化软件开发及其应用［J］. 工程设计学报， 2015， 22（3）： 262-268. doi：10.3785/j.issn.1006-754X.2015.03.010
MAO J， LI Q， XIE M， et al. Multi-objective optimization software development and application［J］. Chinese Journal of Engineering Design， 2015， 22（3）： 262-268.
doi: 10.3785/j.issn.1006-754X.2015.03.010
[20]   QIAN W Y， LI M. Convergence analysis of standard particle swarm optimization algorithm and its improvement［J］. Soft Computing， 2018， 22（12）： 4047-4070.
[21]   HOU G Y， XU Z D， LIU X， et al. Improved particle swarm optimization for selection of shield tunneling parameter values［J］. Computer Modeling in Engineering & Sciences， 2019， 118（2）： 317-337.
[22]   CHANG W D. An improved PSO algorithm for solving nonlinear programing problems with constrained conditions［J］. International Journal of Modeling， Simulation， and Scientific Computing， 2021， 12（1）： 2150001.
[23]   ZHANG J B， ZHENG Y， QI D K. Deep spatio-temporal residual networks for citywide crowd flows prediction［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2017， 31（1）： 1655-1661.
[24]   CHO K， VAN MERRIENBOER B， GULCEHRE C， et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation［C］//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha， Oct. 25-29， 2014.
[25]   彭桐歆， 韩勇， 王程， 等. 面向短时地铁客流量预测的混合深度学习模型［J］. 计算机工程， 2022， 48（5）： 297-305.
PENG T X， HAN Y， WANG C， et al. Hybrid deep-learning model for short-term metro passenger flow prediction［J］. Computer Engineering， 2022， 48（5）： 297-305.
[26]   查玉坤， 张其林， 赵永标， 等. 基于三维卷积和CLSTM神经网络的水产养殖溶解氧预测［J］. 应用科学学报， 2021， 39（4）： 615-626.
ZHA Y K， ZHANG Q L， ZHAO Y B， et al. Prediction of dissolved oxygen in aquaculture based on 3D convolution and CLSTM neural network［J］. Journal of Applied Sciences， 2021， 39（4）： 615-626.
[27]   DIBA A L， FAYYAZ M， SHARMA V， et al. Temporal 3D ConvNets： new architecture and transfer learning for video classification［EB/OL］. （2017-11-22） ［2024-02-01］. .
[28]   WANG Y B， GAO Z F， LONG M S， et al. PredRNN++： towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning［EB/OL］. （2018-11-19） ［2024-02-01］. .
[29]   刘文华. 基于机器学习的火力发电蒸汽量预测方法研究［D］. 太原： 太原科技大学， 2019.
LIU W H. Research on prediction method of steam volume of thermal power generation based on machine learning［D］. Taiyuan： Taiyuan University of Science and Technology， 2019.
[30]   LIU L J， WANG L， YU Z. Remaining useful life estimation of aircraft engines based on deep convolution neural network and LightGBM combination model［J］. International Journal of Computational Intelligence Systems， 2021， 14（1）： 165.
No related articles found!