CAJ | 학술논문

针对供热过程的特点及节能控制的需要,提出基于RBF神经网络的时间序列交叉供热负荷预报法.首先对现场实测的供热负荷数据进行预处理,取得建立预报模型所需的负荷样本阵列;随后,应用自相关法求取RBF神经网络的输入维数,并分别建立时间序列的横向及纵向预报模型;最后用最小二乘法求出横向与纵向负荷预报的交叉权系数,得到RBF神经网络的时间序列交叉预报模型.仿真结果表明,RBF神经网络交叉负荷预报的精度高于横向负荷预报及纵向负荷预报,其实时性要优于BP神经网络交叉负荷预报.
침대공열과정적특점급절능공제적수요,제출기우RBF신경망락적시간서렬교차공열부하예보법.수선대현장실측적공열부하수거진행예처리,취득건립예보모형소수적부하양본진렬;수후,응용자상관법구취RBF신경망락적수입유수,병분별건립시간서렬적횡향급종향예보모형;최후용최소이승법구출횡향여종향부하예보적교차권계수,득도RBF신경망락적시간서렬교차예보모형.방진결과표명,RBF신경망락교차부하예보적정도고우횡향부하예보급종향부하예보,기실시성요우우BP신경망락교차부하예보.
According to the characteristics of heat supply and the demands of energy-saving control, heat load forecasting based on RBF neural network and time series crossover is proposed. Firstly,field measured data are pretreated to generate the load series which is used to found forecasting model. Then autocorrelation method is applied to determine the dimensions of the input vectors of the RBF neural network. Meanwhile, the horizontal and vertical forecasting models based on RBF neural network are established respectively. Finally,the crossover weight coefficients of the horizontal and vertical forecasting models are calculated by using the least-squares method. And the time series crossover forecasting model is obtained. Through comparing the simulation results, the accuracy of crossover forecasting is superior to horizontal and vertical forecasting,and the real-time ability of RBF neural network crossover forecasting is also better than BP neural network crossover forecasting.