Combination forecast research on water demand based on the grey theory and regression analysis | 基于灰色理论和回归分析的需水量组合预测研究
2010
Zhang Qian, Northwest Agriculture and Forestry University, Yangling (China) | Shen Li, The Bureau of Water Resources of Shenzhen Municipality, Shenzhen (China) | Cai Huanjie, Northwest Agriculture and Forestry University, Yangling (China)
中国人. 建立精度更高的需水量预测模型，为水资源规划提供理论依据。建立基于灰色预测和线性回归预测的需水量组合预测模型，以深圳大鹏半岛需水量为例，对组合预测模型的预测结果与单独采用灰色预测、线性回归模型预测的结果进行了比较。单独采用灰色预测模型和线性回归模型进行预测的平均误差分别为6.5%和2.5%，而基于灰色预测和线性回归的组合预测模型的平均误差仅为1%。组合模型的预测精度较单一模型的预测精度明显提高，并且该模型可以更全面地反映需水量的变化规律。
显示更多 [+] 显示较少 [-]英语. A water demand forecast model with higher precision was established in order to provide a theoretical basis for the water rescource planning. We established the combination of grey prediction and linear regression model to forecast the demand of water, by applying the water demand forecast of Dapeng Peninsula in Shenzhen, and the predicted results of the combination model with a separate grey prediction, and the linear regression model to predict the results were compared. Using the grey prediction model and a linear regression model had an average error of respectively 6.5%, 2.5%, while the combination of grey prediction and linear regression forecasting, had an average error of only 1%. The combination model prediction accuracy is more greatly improved than a single model and the model can more fully reflect the changing rule of water demand.
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