摘要
湿球温度是电力工程中常用的气象设计参数,而目前气象站安装的地面气象自动观测设备中无湿球温度观测工具,且已有的湿球温度计算方法存在不足。为了满足工程设计需要,分析了湿球温度与干球温度、相对湿度、大气压强及平均风速等4个气象参数的非线性关系,建立了基于LM-BP神经网络的湿球温度计算模型,并将其应用于潍坊气象站湿球温度计算中。结果表明,该模型计算精度较高,且较为合理地反映了湿球温度与干球温度等影响因子之间复杂的非线性关系。
The wet-bulb temperature is a common weather parameter in power engineering design, but al present most ground automatic weather observation equipments installed in the weather station do not include the wet-bulb tem- perature observation equipment. The common wet bulb temperature calculation methods have shortcomings. In order to meet the needs in engineering design, this paper analyzes the non-linear relationship between wet-bulb temperature and four meteorological parameters, which include dry-bulb temperature, relative humidity, atmospheric pressure and wind speed. And then it established a wet-bulb temperature calculation model based on Levenberg-Marquardt BP neural net work. Finally, this model is applied to calculate wet bulb temperature in Weifang weather station. The results show that the proposed model has high precision and it can well refled the non linear relationship between wet-bulb temperature and dry-bulb temperature.
出处
《水电能源科学》
北大核心
2013年第1期164-166,共3页
Water Resources and Power
关键词
LM算法
BP神经网络
湿球温度
非线性关系
Levenberg-Marquardt algorithm
BP neural networks
wet-bulb temperature
nonlinear relation
