Approximation of System Components for Pump Scheduling Optimisation

© 2015 The Authors. Published by Elsevier Ltd.The operation of pump systems in water distribution systems (WDS) is commonly the most expensive task for utilities with up to 70% of the operating cost of a pump system attributed to electricity consumption. Optimisation of pump scheduling could save 10-20% by improving efficiency or shifting consumption to periods with low tariffs. Due to the complexity of the optimal control problem, heuristic methods which cannot guarantee optimality are often applied. To facilitate the use of mathematical optimisation this paper investigates formulations of WDS components. We show that linear approximations outperform non-linear approximations, while maintaining comparable levels of accuracy

Gaussian-process-based demand forecasting for predictive control of drinking water networks

This paper focuses on water demand forecasting for predictive control of Drinking Water Networks (DWN) in the short term by using Gaussian Process (GP). For the predictive control strategy, system states in a finite horizon are generated by a DWN model and demands are regarded as system disturbances. The goal is to provide a demand estimation within a given confidence interval. For the sake of obtaining a desired forecasting performance, the forecasting process is carried out in two parts: the expected part is forecasted by Double-Seasonal Holt-Winters (DSHW) method and the stochastic part is forecasted by GP method. The mean value of water demand is firstly estimated by DSHW while GP provides estimations within a confidence interval. GP is applied with random inputs to propagate uncertainty at each step. Results of the application of the proposed approach to a real case study based on the Barcelona DWN have shown that the general goal has been successfully reached.Peer ReviewedPostprint (author’s final draft

Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks

Efficient management of a drinking water network reduces the economic costs related to water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economic costs. Therefore, short-term forecasts are a key issue in the performance of MPC applied to water distribution networks. However, the short-term horizon demand forecast in a horizon of 24 hours in an hourly based scale presents some challenges as the water consumption can change from one day to another, according to certain patterns of behavior (e.g., holidays and business days). This paper focuses on the problem of forecasting water demand for the next 24 hours. In this work, we propose to use a bank of models instead of a single model. Each model is designed for forecasting one particular hour. Hourly models use artificial neural networks. The architecture design and the training process are performed using genetic algorithms. The proposed approach is assessed using demand data from the Barcelona water network.Peer ReviewedPostprint (author's final draft

Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

open access articleShort-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 minute time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases

Multi-model prediction for demand forecast in water distribution networks

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracyPeer ReviewedPostprint (published version

Modelo para el pronóstico de la demanda real de agua potable en Quito

Se propone desarrollar, calibrar y validar un modelo matemático que pronostique la demanda real de agua potable en Quito con horizontes de corto, mediano y largo plazos, considerando las principales variables que actúan en el suministro de agua. Se supone que la demanda de agua potable en Quito puede ser pronosticada ya que su fluctuación y crecimiento están definidos en gran parte por variables meteorológicas, urbanísticas o de ordenamiento territorial, demográficas, económicas, sociales y la autocorrelación propia de la demanda. El modelo de pronóstico de la demanda de agua corresponde al estudio y análisis de una serie temporal o vector X1 de 2557 datos de caudales medios diarios (litros/seg.) entregados por la Planta de Tratamiento de Agua Potable de Bellavista, PTAP de Quito, desde enero 2007 hasta diciembre 2013. Para el análisis de la serie temporal o vector X1 se utilizan varias herramientas estadísticas y la transformada rápida de Fourier que nos ayuda a determinar las periodicidades del fenómeno. Se concluye obteniendo explícitamente la función de pronóstico de agua potable buscada. La validación de la función de demanda obtenida se la hará en la segunda etapa de la investigación, usando los datos que van desde el inicio de 2014 hasta el año 2016

Adaptive water demand forecasting for near real-time management of smart water distribution systems

This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EANNs). It is implemented in a fully automated, data-driven and self-learning Demand Forecasting System (DFS) that is readily transferable to practice. The main characteristics of the DFS are: (a) continuous adaptability to ever changing water demand patterns and (b) generic and seamless applicability to different demand signals. The DFS enables applying two alternative WDF approaches. In the first approach, multiple EANN models are used in parallel to separately forecast demands for different hours of the day. In the second approach, a single EANN model with a fixed forecast horizon (i.e., 1h) is used in a recursive fashion to forecast demands. Both approaches have been tested and verified on a real-life WDS in the United Kingdom (UK). The results obtained illustrate that, regardless of the WDF approach used, the novel methodology allows accurate forecasts to be generated thereby demonstrating the potential to yield substantial improvements to the state-of-the-art in near real-time WDS management. The results obtained also demonstrate that the multiple-EANN-models approach slightly outperforms the single-EANN-model approach in terms of WDF accuracy. The single-EANN-model approach, however, still enables achieving good WDF performance and may be a preferred option in engineering practice as it is easier to setup/implement. © 2014 Elsevier Ltd.UK Engineering and Physical Sciences Research Counci