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首页> 外文期刊>Journal of Zhejiang University. Science, A >Utility water supply forecast via a GM (1,1) weighted Markov chain
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Utility water supply forecast via a GM (1,1) weighted Markov chain

机译：通过GM（1,1）加权Markov链的电气供水预测

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This paper describes the procedure of using the GM (1,1) weighted markov chain (GMWMC) to forecast the utility water supply, a quantity that usually has significant temporal variability. The GMWMC is formulated into five steps: (1) use GM (1,1) to fit the trend of the data, and obtain the relative error of the fitted values; (2) divide the relative error into ‘state’ data based on pre-set intervals; (3) calibrate the weighted markov chain model: herein the parameters are the pre-set interval and the step of transition matrix (TM); (4) by using auto-correlation coefficient as the weight, the markov chain provides the prediction interval. Then the mid-value of the interval is selected as the relative error for the data. Upon combining the data and its relative error, the predicted magnitude in a specific time period is obtained; and, (5) validate the model. Commonly, static intervals are used in both model calibration and validation stages, usually causing large errors. Thus, a dynamic adjustment interval (DAI) is proposed for a better performance. The proposed procedure is described and demonstrated through a case study, which shows that the DAI can usually achieve a better performance than the static interval, and the best TM may exist for certain data.
机译：本文介绍了使用GM（1,1）加权Markov链（GMWMC）预测本型水供应量，这一数量通常具有显着的时间变异性。 GMWMC配制成五步：（1）使用GM（1,1）符合数据的趋势，并获得拟合值的相对误差; （2）将相对误差划分为基于预设的间隔的“状态”数据; （3）校准加权Markov链模型：这里，参数是预设间隔和转换矩阵（TM）的步骤; （4）通过使用自动相关系数作为权重，Markov链提供预测间隔。然后选择间隔的中值作为数据的相对误差。结合数据及其相对误差时，获得了特定时间段中的预测幅度;并且（5）验证模型。通常，静态间隔用于模型校准和验证阶段，通常会导致大错误。因此，提出了一种动态调整间隔（DAI）以获得更好的性能。通过案例研究描述和证明了所提出的程序，表明DAI通常可以实现比静态间隔更好的性能，并且对于某些数据可能存在最佳TM。

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