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Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

机译：短期需水预测模型的概述，比较评估和建议

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The stochastic nature of water consumption patterns during the day and week varies. Therefore, to continually provide water to consumers with appropriate quality, quantity and pressure, water utilities require accurate and appropriate short-term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times series models (i.e., autoregressive (AR), moving average (MA), autoregressive-moving average (ARMA), and ARMA with exogenous variable (ARMAX)) introduced by Box and Jenkins (1970), feed-forward back-propagation neural network (FFBP-NN), and hybrid model (i.e., combined forecasts from ARMA and FFBP-NN) are compared with each other for a common set of data. Akaike information criterion (AIC), originally proposed by Akaike (1974) is used to estimate the quality of each short-term forecasting model. Furthermore, Nash?￠????Sutcliffe (NS) model efficiency coefficient proposed by Nash?￠????Sutcliffe (1970), root mean square error (RMSE) and mean absolute percentage error (MAPE) are the forecasting statistical terms used to assess the predictive performance of the models. Lastly, as regards the selection of an accurate and appropriate STWD forecasting model, this paper provides recommendations and future work based on the forecasts generated by each of the predictive models considered.
机译：白天和一周中用水模式的随机性各不相同。因此，为了持续向消费者提供适当的质量，数量和压力的水，自来水公司需要准确，适当的短期需水量（STWD）预测。鉴于此，提出了STWD预测的预测方法的概述。在此基础上，研究了不同方法对替代预测模型性能的比较评估。 Box和Jenkins（1970）提出的Times系列模型（即自回归（AR），移动平均（MA），自回归移动平均（ARMA和具有外生变量的ARMA（ARMAX））），前馈反向传播神经网络（FFBP-NN）和混合模型（即来自ARMA和FFBP-NN的组合预测）相互比较以获取一组公共数据。 Akaike（1974）最初提出的Akaike信息标准（AIC）用于估计每个短期预测模型的质量。此外，由纳什·萨特克利夫（1970）提出的纳什·萨特克利夫（NS）模型效率系数，均方根误差（RMSE）和平均绝对百分比误差（MAPE）是预测统计项。用于评估模型的预测性能。最后，关于选择正确和适当的STWD预测模型，本文根据所考虑的每种预测模型生成的预测提供建议和未来的工作。

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