A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Gagliardi, Francesca; Alvisi, Stefano; Kapelan, Zoran; Franchini, Marco

doi:10.3390/w9070507

Cited by 40 publications

(25 citation statements)

References 36 publications

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“…Gagliardi et al [29] used both homogeneous and non-homogeneous Markov chains to build two different models for water demand forecasting. The results evidenced a better performance for short-term predictions by the homogeneous chain, whilst the non-homogeneous one was in line with the artificial neural networks.…”

Section: Related Workmentioning

confidence: 99%

A Short-Term Data Based Water Consumption Prediction Approach

et al. 2019

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A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper.

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Section: Related Workmentioning

confidence: 99%

A Short-Term Data Based Water Consumption Prediction Approach

et al. 2019

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“…It is useful for STWD operational forecasts to minimise the operating cost of pumping stations [1,6,15,16,18]. However, as regards influencing future water demand, a major criticism of UTS predictive models is their failure to account for the effects of changing exogenous variables [11,18].…”

Section: Recommendations Of Stwd Forecasting Models and Future Workmentioning

confidence: 99%

“…STWD forecasting is an important component of the successful operation, management, and optimisation of any existing WDS. As a result, the selection of an accurate and appropriate STWD forecasting model is useful for [1,6,[8][9][10][11][12][13][14][15][16]]:…”

Section: Introductionmentioning

confidence: 99%

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

Anele

Hamam

Abu‐Mahfouz

et al. 2017

Water

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Abstract: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.

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“…Precise short-term prediction of urban water demand provides guidance for the planning and management of water resources and plays an important role in the economic operation of a water supply system. Therefore, various water demand prediction models, such as support vector regression (SVR) [1,2], random forests regression [3], artificial neural network (ANN) [4], Markov chain model [5], and hybrid models [6][7][8][9], have been widely developed in the past few decades. Research regarding water demand prediction generally focuses on methods involving ANN, which are nonparametric data-driven approaches applicable for building nonlinear mapping from input to output variables for estimating nonlinear continuous functions with an arbitrary accuracy [10].…”

Section: Introductionmentioning

confidence: 99%

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Zhang

Long

et al. 2019

Water

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Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for the prediction of hourly urban water demand. The CDBESN model uses a continuous deep belief network (CDBN) as the feature extraction algorithm and an echo state network (ESN) as the regression algorithm. The new architecture can model actual water demand data with fast convergence and global optimization ability. The prediction capacity of the CDBESN model is tested using historical hourly water demand data obtained from an urban waterworks in Zhuzhou, China. The performance of the proposed model is compared with those of ESN, continuous deep belief neural network, and support vector regression models. The correlation coefficient (r2), normalized root-mean-square error (NRMSE), and mean absolute percentage error (MAPE) are adopted as assessment criteria. Forecasting results obtained in the testing stage indicate that the CDBESN model has the largest r2 value of 0.995912 and the smallest NRMSE and MAPE values of 0.027163 and 2.469419, respectively. The prediction accuracy of the proposed model clearly outperforms those of the models it is compared with due to the good feature extraction ability of CDBN and the excellent feature learning ability of ESN.

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A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Cited by 40 publications

References 36 publications

A Short-Term Data Based Water Consumption Prediction Approach

A Short-Term Data Based Water Consumption Prediction Approach

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

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

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