基于自适应过滤与BP神经网络的城市时用水量组合预测模型
发布时间：2018-07-15 11:40
【摘要】：城市短期时用水量预测是城市管网进行优化调度决策的基础,用水量预测的准确性与稳定性更是关系着城市供水正常运行管理的关键。根据某市的用水数据,分别利用时间序列预测方法以及解释性预测方法对城市短期时用水量进行预测。综合两种预测方法的优点,提出了基于权系数优化理论的组合预测法。通过实例预测结果比较,用水量组合预测法模型较单项预测模型有更高的准确性和稳定性。
[Abstract]:Short-term urban water consumption prediction is the basis of optimal dispatching decision of urban pipe networks, and the accuracy and stability of water consumption prediction is the key to the normal operation and management of urban water supply. According to the water use data of a certain city, the time series forecasting method and the explanatory forecasting method are used to predict the short-term water consumption of the city. Combining the advantages of two forecasting methods, a combined prediction method based on weight coefficient optimization theory is proposed. By comparing the forecasting results with practical examples, the combined forecasting model of water consumption has higher accuracy and stability than the single forecasting model.
【作者单位】： 沈阳建筑大学市政与环境工程学院;
【基金】：住房和城乡建设部2016年科技计划项目(2016-K3-026) 辽宁省自然科学基金项目(20170540744) 辽宁省教育厅科技计划项目(LJZ2016025)
【分类号】：TU991.31
[Abstract]:Short-term urban water consumption prediction is the basis of optimal dispatching decision of urban pipe networks, and the accuracy and stability of water consumption prediction is the key to the normal operation and management of urban water supply. According to the water use data of a certain city, the time series forecasting method and the explanatory forecasting method are used to predict the short-term water consumption of the city. Combining the advantages of two forecasting methods, a combined prediction method based on weight coefficient optimization theory is proposed. By comparing the forecasting results with practical examples, the combined forecasting model of water consumption has higher accuracy and stability than the single forecasting model.
【作者单位】： 沈阳建筑大学市政与环境工程学院;
【基金】：住房和城乡建设部2016年科技计划项目(2016-K3-026) 辽宁省自然科学基金项目(20170540744) 辽宁省教育厅科技计划项目(LJZ2016025)
【分类号】：TU991.31
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