摘要
天然气负荷预测天然气负荷准确预测是天然气管网的优化的基础。短期天然气负荷变化呈现伪周期性和随机性。为了提高天然气负荷的预测精度,提出一种基于Elman神经网络天然气负荷预测模型,并采集某企业的天然气负荷数据进行仿真,并BP神经网络预测模型进行了对比分析,仿真结果验证所建立预测模型是可行且有效的,具有一定的应用价值。
Gas load prediction plays critical roles in the efficient operation of gas pipeline networks optimization.As shortterm gas load changes periodicly and randomly.To increase gas load prediction accuracy,short-term gas load forecasting model based on Elman neural network is established in this paper,and the model performance is verified by some gas load data from an enterprise.Compared with BP network,the results show that Elman developed has the feasibility and effectiveness in the short-term gas load prediction.
出处
《工业控制计算机》
2016年第4期87-88,共2页
Industrial Control Computer
