[1]温焱明,熊 波,牛火平,等.基于数据驱动的供热系统优化调控研究[J].工业仪表与自动化装置,2024,(02):49-53+112.[doi:DOI:10.19950/j.cnki.CN61-1121/TH.2024.02.009]
　WEN Yanming,XIONG Bo,NIU Huoping,et al.Research on data-driven optimization and regulation of heating systems[J].Industrial Instrumentation & Automation,2024,(02):49-53+112.[doi:DOI:10.19950/j.cnki.CN61-1121/TH.2024.02.009]
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基于数据驱动的供热系统优化调控研究()

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2024年02期
页码:
49-53+112
栏目:
出版日期:
2024-04-15

文章信息/Info

Title:
Research on data-driven optimization and regulation of heating systems
文章编号:
1000-0682（2024）02-0049-05
作者:
温焱明; 熊 波; 牛火平; 李祥麟; 何树华
（中山嘉明电力有限公司，广东 中山 528451）
Author(s):
WEN Yanming; XIONG Bo; NIU Huoping; LI Xianglin; HE Shuhua
(Zhongshan Jiaming Electric Power Co., Ltd., Guangdong Zhongshan 528451, China）
关键词:
供热系统; 机器学习; 数据驱动; 优化调控; 节能
Keywords:
heating system;  machine learning;  data driven;  optimize regulation;  energy conservation
分类号:
TU995
DOI:
DOI:10.19950/j.cnki.CN61-1121/TH.2024.02.009
文献标志码:
A
摘要:
针对传统供热系统运行过程中调节方式简单造成能源浪费等情况,基于系统运行数据构建了以输配能耗最小为目的的机器学习调控模型,提出一种基于数据驱动的供热系统优化调控方法。通过机器学习建立了单元阀门调控模型、短期热负荷预测模型和换热站流量调控模型,对各模型进行研究分析,得到最优模型参数组合。对优化前后的运行数据进行实验对比,结果表明：在供热量不变的情况下,优化后平均循环流量和泵耗明显降低,泵耗的节能率达到38%,具有良好的节能效果。
Abstract:
In view of the situation that the simple regulation method in the operation of traditional heating system causes energy waste, a machine learning regulation model aiming at minimizing the energy consumption of transmission and distribution is constructed based on the system operation data, and a data driven optimization regulation method for heating system is proposed. A unit valve control model, short-term heat load prediction model, and heat exchange station flow control model were established through machine learning. The optimal model parameter combination was obtained through research and analysis of each model. Through experimental comparison of operational data before and after optimization, the results show that: under the condition of constant heat supply, the average circulating flow rate and pump consumption after optimization are significantly reduced, and the energy-saving rate of pump consumption reaches 38%, which has a good energy-saving effect.

参考文献/References:

[1]沈振锋,云艳玲,赵美丽.集中供热利用梁式窑导热油余热探讨[J].盐科学与化工,2023,52(07):45-47.

[2] WANG H , WANG H , HAIJIAN Z , et al. Optimization modeling for smart operationof multi-source district heating with distributed variable-speed pumps[J].Energy, 2017,138.
[3] 李琳.“双碳”目标下的供热系统节能分析[J].能源与节能,2023(09):79-81.
[4] 方丽华.浅析造成集中供热系统调节滞后性的因素[J].资源节约与环保,2023(08):145-148.
[5] ZHONG W, FENG E, LIN X, et al. Research on data-driven operation control ofsecondary loop of district heating system[J]. Energy, 2022,239.
[6] 李欣.多热源耦合供热系统运行策略研究[J].节能与环保,2023(07):41-43.
[7] 赵笑言,郑立军,林海卫等.基于机器自学习的供热系统热负荷预测[J].节能,2023,42(08):81-84.
[8] Kodimoole Sahana;AnindityaKaur. Artificial intelligence and machine learning: The new frontier of digital dentistry [J]. Journal of Dental and Orofacial Research. 2020,16(1): 58-63.
[9] 刘晶,李超然,张建楠,等.基于融合驱动的余热阀门控制优化方法[J].热力发电,2023,52(10):176-186.
[10] LIU Y X,HONG H P. Data-Driven Approach for Generating Tricomponent Nonstationary Non-Gaussian Thunderstorm Wind Records Using Continuous Wavelet Transforms and S-Transform[J].Journal of Structural Engineering,2023,149(12).
[11] KACIROTI N A .Letter to the Editor in Response to "Z-Score Burden Metric: A Method for Assessing Burden of Injury and Disease"[J].American journal of preventive medicine, 2023, 64(2):301.
[12] 李明飞,吴军超.基于混合核函数的LSSVM在GNSS高程拟合中的应用[J].工程勘察,2019,47(12):64-68.
[13] 杨朝,何明浩,韩俊,等.一种新的支持向量机核函数评估方法[J].雷达科学与技术,2017,15(06):630-634.
[14] 杨亮,王谊.应用改进RBF神经网络的室内环境舒适度评价[J].微型电脑应用,2021,37(07):86-89.
[15] 翟莹莹,左丽,张恩德.基于参数优化的RBF神经网络结构设计算法[J].东北大学学报(自然科学版),2020,41(02):176-181+187.

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备注/Memo

备注/Memo:
收稿日期：2023-10-13第一作者：温焱明（1983—），男，广东普宁人，硕士，高级工程师，研究方向为仪器仪表控制工程。E-mail：zaq12131415@126.com
更新日期/Last Update: 1900-01-01