{"title":"Heat Distribution of Heat Exchange Station in District Heating System based on Load Forecasting","authors":"Bingwen Zhao, Hanyu Zheng, Ruxue Yan","doi":"10.1134/s0040601524040086","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>District heating system is the main way of heating in cities and towns in China. The development of district heating system still has the problems of low intelligence and low control accuracy, and there is the imbalance of heat supply and demand in heat distribution. Resulting in the energy consumed by the district heating system can account for more than half of the total energy consumption of the building. In order to alleviate this imbalance, this paper studies the control of heat distribution of each heat exchange station in the primary network. The heat model of primary network is established by recurrent neural network (RNN), and the data set used for modeling is the operation data of heat exchange station in reality. Combined with the heat load prediction model, a heat distribution strategy was proposed to optimize the primary flow of the heat exchange station. According to the predicted value, chaotic particle swarm optimization (CPSO) algorithm is used to optimize the primary flow sequence of each heat exchange station, and then the primary flow is adjusted to control the heat distribution of the secondary network. Finally, Simulink simulation model is used to simulate the water supply temperature of the secondary side of the heat exchange station. And analyze the operation status of the secondary side, the results verify the effectiveness of the strategy. The model simulation results show that the heat distribution scheme proposed in this paper can effectively distribute the heat of the heat exchange station according to the heat demand.</p>","PeriodicalId":799,"journal":{"name":"Thermal Engineering","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Thermal Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s0040601524040086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
District heating system is the main way of heating in cities and towns in China. The development of district heating system still has the problems of low intelligence and low control accuracy, and there is the imbalance of heat supply and demand in heat distribution. Resulting in the energy consumed by the district heating system can account for more than half of the total energy consumption of the building. In order to alleviate this imbalance, this paper studies the control of heat distribution of each heat exchange station in the primary network. The heat model of primary network is established by recurrent neural network (RNN), and the data set used for modeling is the operation data of heat exchange station in reality. Combined with the heat load prediction model, a heat distribution strategy was proposed to optimize the primary flow of the heat exchange station. According to the predicted value, chaotic particle swarm optimization (CPSO) algorithm is used to optimize the primary flow sequence of each heat exchange station, and then the primary flow is adjusted to control the heat distribution of the secondary network. Finally, Simulink simulation model is used to simulate the water supply temperature of the secondary side of the heat exchange station. And analyze the operation status of the secondary side, the results verify the effectiveness of the strategy. The model simulation results show that the heat distribution scheme proposed in this paper can effectively distribute the heat of the heat exchange station according to the heat demand.
AbstractDistrict heating system is the main way of heating in cities and towns in China.区域供热系统的发展仍存在智能化程度低、控制精度低、供热供需不平衡等问题。导致区域供热系统的能耗占建筑总能耗的一半以上。为了缓解这种不平衡，本文研究了一次网中各换热站的热量分配控制。采用递归神经网络（RNN）建立一次网热量模型，建模数据集为现实中换热站的运行数据。结合热负荷预测模型，提出了优化换热站一次流量的热分配策略。根据预测值，采用混沌粒子群优化算法（CPSO）优化各换热站的一次流量顺序，然后通过调整一次流量来控制二次网的热量分配。最后，利用 Simulink 仿真模型模拟换热站二次侧的供水温度。并分析二次侧的运行状态，结果验证了该策略的有效性。模型仿真结果表明，本文提出的热量分配方案能根据热量需求有效分配换热站的热量。
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