A Comparison of Short-Term Water Demand Forecasting Models

Abstract

This paper presents a comparison of different short-term water demand forecasting models. The comparison regards six models that differ in terms of: forecasting technique, type of forecast (deterministic or probabilistic) and the amount of data necessary for calibration. Specifically, the following are compared: a neural-network based model (ANN_WDF), a pattern-based model (Patt_WDF), two pattern-based models relying on the moving-window technique (αβ_WDF and Bakk_WDF), a probabilistic Markov chain-based model (HMC_WDF) and a naïve benchmark model. The comparison is made by applying the models to seven real-life cases, making reference to the water demands observed over 2 years in district-metered areas/ water distribution networks of different sizes serving a different number and type of users. The models are applied in order to forecast the hourly water demands over a 24-h time horizon. The comparison shows that a) models based on different techniques provide comparable, mediumhigh forecasting accuracies, but also that b) short-term water demand forecasting models based on moving-window techniques are generally the most robust and easier to set up and parameterize.

Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

“…Ridge regression was chosen for its capacity to reduce multicollinearity caused by sensors displaying similar diurnal water demand patterns. Without regularization, thus with λ = 0, this would lead to a nearly singular matrix X T X. Regularization improves efficiency of the nowcasting and reduces variance, by introducing a small amount of bias (Pacchin et al 2019). For the practical application of water demand nowcasting, the small amount of bias introduced is deemed acceptable in the bias-variance tradeoff, as it prevents the prediction from being over-dependent on the signal of a single exogenous sensor.…”

“…Recent methods make use of neural networks (NN) or other supervised machine learning methods, or hybrid methods that combine NN with univariate/regression forecasting models (Babel and Shinde 2011;Bai et al 2014;Xu et al 2018;Pacchin et al 2019). Similar to exogenous ARMA models, these methods are capable of incorporating exogenous data and boast reliable forecasts, but require extensive historical data for training and are accompanied by large forecast uncertainties, which cannot always be quantified (Hutton and Kapelan 2015b;Anele et al 2017).…”

Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

“…A hybrid model contains two or more techniques; one of them would work as the primary model, while others would act as pre-processing or post-processing approaches [37]. Hybrid models have been used to simulate municipal water demand using different techniques and in different scenarios, and the results have revealed that these models are robust and insightful, e.g., Altunkaynak and Nigussie [38], Seo et al [24], Pacchin et al [39], Ebrahim Banihabib and Mousavi-Mirkalaei [2] and Rasifaghihi et al [40].…”

Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model

“…Urban water demand predictions are often important to the sustainable management of water supply systems for a range of purposes, including system design, maintenance and operation (Billings and Jones, 2008;Zheng et al 2016Zheng et al , 2017Qi et al, 2018). Accurate urban demand forecasts have become even more vital for many cities in recent years due to the emerged water crisis as a result of rapid urbanization and climate change, as well as driven by the need of real-time system operation (Hutton and Kapelan, 2014;Pacchin et al, 2019). This, consequently, has motived intensive studies to develop models for urban demand prediction, thereby enabling an effective water usage planning and scheduling (Pacchin et al, 2019).…”

“…Accurate urban demand forecasts have become even more vital for many cities in recent years due to the emerged water crisis as a result of rapid urbanization and climate change, as well as driven by the need of real-time system operation (Hutton and Kapelan, 2014;Pacchin et al, 2019). This, consequently, has motived intensive studies to develop models for urban demand prediction, thereby enabling an effective water usage planning and scheduling (Pacchin et al, 2019).…”