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作 者:黄刘松 储岳中 张飞 周明琴 吴慧林[1] HUANG Liu-song;CHU Yue-zhong;ZHANG Fei;ZHOU Ming-qin;WU Hui-lin(School of Software and Internet,Ma′anshan Teacher′s College,Ma′anshan 243041,China;College of Computer Science and Technology,Anhui University of Technology,Ma′anshan 243032,China;Information Department,Anhui University of Technology,Ma′anshan 243032,China;Information Technology Department,Guodian Nanjing Automation Co.,Ltd,Nanjing 211100,China)
机构地区:[1]马鞍山师范高等专科学校软件与互联网学院,安徽马鞍山243041 [2]安徽工业大学计算机科学与技术学院,安徽马鞍山243032 [3]安徽工业大学信息化处,安徽马鞍山243032 [4]国电南京自动化股份有限公司信息技术事业部,南京211100
出 处:《西安航空学院学报》2022年第1期71-75,共5页Journal of Xi’an Aeronautical Institute
基 金:安徽省高校自然科学基金研究项目(KJ2017ZD05);安徽高校自然科学研究重点项目(KJ2017A069);马鞍山师范高等专科学校自然科学研究重点项目(2021xjzdky11);安徽省质量工程项目(2019mooc381)。
摘 要:为了更好地对电厂中机组的能耗问题进行分析研究,根据电厂不同工况的参数指标,建立电力企业能耗仿真BP神经网络模型。归一化处理电厂运行过程中各类传感器采集的数据,采用负荷、环境温度、排烟温度、背压、含氧量等指标,并加入时序历史能耗作为输入参数;利用电力企业短时能耗作为输出参数;通过采用不同时间窗口的连续时序能耗参数指标和热力学相关参数作为输入,在神经网络中不同中间层的隐层节点数下进行仿真实验。结果表明,基于时序历史能耗数据的电厂指标参数在包含21个隐层节点数的BP神经网络模型上能够在线仿真出精度较高的短时供电能耗数据。所建的电力能耗预测模型将为后续电厂的节能减排、负荷优化,提供理论参数支撑。In order to further study and analyze energy consumption of units in power plant,a BP neural network model for energy consumption simulation of power enterprises is established according to the parameter indexes of different working conditions of power plant.Data collected by various sensors was normalized during the running of the power plant,indicators such as load,ambient temperature,exhaust gas temperature,back pressure and oxygen content was adopted.With time series historical energy consumption being input parameters,the short-time energy consumption of electric power enterprises was taken as the output parameter.By using the continuous time series energy consumption parameters and thermodynamic parameters of different time windows as input,the simulation experiments were carried out in the hidden layer nodes of different intermediate layers in the neural network.The results show that the power plant index parameters based on time series historical energy consumption data can simulate online the short-term energy consumption data with high accuracy on the BP neural network model with 21 hidden layer nodes.The power energy consumption prediction model will provide useful theoretical parameters for energy conservation,emission reduction and load optimization of power plants.
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