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

Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks

PublishedThis paper presents an artificial neural network-based model of domestic water consumption. The model is based on real-world data collected from smart meters, and represents a step toward being able to model real-time smart meter data. A range of input schemas are examined, including real meter readings and summary statistics derived from readings, and it is found that the models can predict some consumption but struggle to accurately match in cases of peak usage

Smart Water Demand Forecasting: Learning from the Data

Accurate forecasts of demand are essential for water utilities in order to manage, plan, and optimize the operation of their network. This work aims to develop a new method for short- term water demand forecasting by utilizing a new data-driven approach based on Random Forests, as well as consumption recordings, household, and socio-economic characteristics, and weather data. Initial results, obtained on real-life consumption data from the UK, demonstrate the potential of this method and show the importance of disaggregating consumption when attempting to determine the influence of weather on water demand. In this study, adding weather input to the model achieved improved forecasting accuracy, especially for the aggregation of properties with medium occupancy and affluent residents during summer months

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

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

Real-time multiobjective optimization of operation of water supply systems

The need for more efficient use of energy in water distribution systems is increasing constantly due to increasing energy prices. A new methodology for optimized real-time operation of a water distribution system is developed and presented here. The methodology is based on the integration of three models: (1) real-time demand forecasting model, (2) hydraulic simulation model of the system, and (3) optimization model. The optimization process is driven by the cost minimization of the energy used for pumping and the maximization of operational reliability. The latter is quantified using alternative measures into the optimization process in order to mimic the conservative attitude to pump scheduling often adopted by control room operators in real-life systems. Optimal pump schedules were generated by using a multialgorithmgenetically- adaptive-method (AMALGAM), hydraulic simulations are performed by using the EPANET2 model, and demand forecasting was performed by using the recently developed DAN2-H model. A number of other methodological developments are used to enable pump scheduling in real time. The new methodology is tested, verified, and demonstrated on the water distribution system of Araraquara, in the state of São Paulo, Brazil. The results obtained demonstrate that it is possible to achieve substantial energy cost savings (up to 13% relative to historical system operation) while simultaneously maintaining the level of supply reliability obtained by manually operating the water system.São Paulo Research Foundation (FAPESP)Coordination for the Improvement of Higher Education (CAPES)Brazilian Scientific and Technological Development Council (CNPq)Araraquara’s Autonomous Department of Water and Sewage (DAAE-Araraquara, SP, Brazil

Smart Water Management towards Future Water Sustainable Networks

[EN] Water management towards smart cities is an issue increasingly appreciated under financial and environmental sustainability focus in any water sector. The main objective of this research is to disclose the technological breakthroughs associated with water and energy use. A methodology is proposed and applied in a case study to analyze the benefits to develop smart water grids, showing the advantages offered by the development of control measures. The case study showed the positive results, particularly savings of 57 GWh and 100 Mm3 in a period of twelve years when different measures from the common ones were developed for the monitoring and control of water losses in smart water management. These savings contributed to reducing the CO2 emissions to 47,385 t CO2-eq. Finally, in order to evaluate the financial effort and savings obtained in this reference systems (RS) network, the investment required in the monitoring and water losses control in a correlation model case (CMC) was estimated, and, as a consequence, the losses level presented a significant reduction towards sustainable values in the next nine years. Since the pressure control is one of the main issues for the reduction of leakage, an estimation of energy production for Portugal is also presentedRamos, HM.; Mcnabola, A.; López Jiménez, PA.; Pérez-Sánchez, M. (2020). Smart Water Management towards Future Water Sustainable Networks. Water. 12(1):1-13. https://doi.org/10.3390/w12010058S113121Sachidananda, M., Webb, D., & Rahimifard, S. (2016). A Concept of Water Usage Efficiency to Support Water Reduction in Manufacturing Industry. Sustainability, 8(12), 1222. doi:10.3390/su8121222Boyle, T., Giurco, D., Mukheibir, P., Liu, A., Moy, C., White, S., & Stewart, R. (2013). Intelligent Metering for Urban Water: A Review. Water, 5(3), 1052-1081. doi:10.3390/w5031052Ritzema, H., Kirkpatrick, H., Stibinger, J., Heinhuis, H., Belting, H., Schrijver, R., & Diemont, H. (2016). Water Management Supporting the Delivery of Ecosystem Services for Grassland, Heath and Moorland. Sustainability, 8(5), 440. doi:10.3390/su8050440Pérez-Sánchez, M., Sánchez-Romero, F. J., & López-Jiménez, P. A. (2017). Nexo agua-energía: optimización energética en sistemas de distribución. Aplicación ‘Postrasvase Júcar-Vinalopó’ (España). Tecnología y ciencias del agua, 08(4), 19-36. doi:10.24850/j-tyca-2017-04-02Howell, S., Rezgui, Y., & Beach, T. (2017). Integrating building and urban semantics to empower smart water solutions. Automation in Construction, 81, 434-448. doi:10.1016/j.autcon.2017.02.004Mounce, S. R., Pedraza, C., Jackson, T., Linford, P., & Boxall, J. B. (2015). Cloud Based Machine Learning Approaches for Leakage Assessment and Management in Smart Water Networks. Procedia Engineering, 119, 43-52. doi:10.1016/j.proeng.2015.08.851Lombardi, P., Giordano, S., Farouh, H., & Yousef, W. (2012). Modelling the smart city performance. Innovation: The European Journal of Social Science Research, 25(2), 137-149. doi:10.1080/13511610.2012.660325Smart Cities: Strategic Sustainable Development for an Urban World. Sweden: School of Engineering, Blekinge Institute of Technology https://www.diva-portal.org/smash/get/diva2:832150/FULLTEXT01.pdfSmart Cities: Ranking of European Medium-Sized. Vienna, Austria: Centre of Regional Science (SRF), Vienna University of Technology http://www.smart-cities.eu/download/smart_cities_final_report.pdfHellström, D., Jeppsson, U., & Kärrman, E. (2000). A framework for systems analysis of sustainable urban water management. Environmental Impact Assessment Review, 20(3), 311-321. doi:10.1016/s0195-9255(00)00043-3Smart Water Grid. USA: Department of Civil and Environmental Engineering, Colorado State University http://www.engr.colostate.edu/~pierre/ce_old/Projects/Rising%20Stars%20Website/Martyusheva,Olga_PlanB_TechnicalReport.pdfSmart Metering Introduction. Obtained on 12 August 2015, from Alliance for Water Efficiency http://www.allianceforwaterefficiency.org/smart-meter-introduction.aspxNtuli, N., & Abu-Mahfouz, A. (2016). A Simple Security Architecture for Smart Water Management System. Procedia Computer Science, 83, 1164-1169. doi:10.1016/j.procs.2016.04.239Britton, T. C., Stewart, R. A., & O’Halloran, K. R. (2013). Smart metering: enabler for rapid and effective post meter leakage identification and water loss management. Journal of Cleaner Production, 54, 166-176. doi:10.1016/j.jclepro.2013.05.018Sharvelle, S., Dozier, A., Arabi, M., & Reichel, B. (2017). A geospatially-enabled web tool for urban water demand forecasting and assessment of alternative urban water management strategies. Environmental Modelling & Software, 97, 213-228. doi:10.1016/j.envsoft.2017.08.009Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Results.A Guide for Sensor Manufacturers and Water Utilities. Ohio: EPA–Environmental Protection Agency https://www.epa.gov/sites/production/files/2015-06/documents/distribution_system_water_quality_monitoring_sensor_technology_evaluation_methodology_results.pdfSCADA: Supervisory Control and Data Acquision. USA: ISA–The Instrumentation, Systemas and Automation Society https://www.fer.unizg.hr/_download/repository/SCADA-Supervisory_And_Data_Acquisition.pdfCan we make water systems smarter? Opflow http://innovyze.com/news/showcases/SmartWaterNetworks.pdfGurung, T. R., Stewart, R. A., Beal, C. D., & Sharma, A. K. (2015). Smart meter enabled water end-use demand data: platform for the enhanced infrastructure planning of contemporary urban water supply networks. Journal of Cleaner Production, 87, 642-654. doi:10.1016/j.jclepro.2014.09.054Romano, M., & Kapelan, Z. (2014). Adaptive water demand forecasting for near real-time management of smart water distribution systems. Environmental Modelling & Software, 60, 265-276. doi:10.1016/j.envsoft.2014.06.016Samora, I., Franca, M. J., Schleiss, A. J., & Ramos, H. M. (2016). Simulated Annealing in Optimization of Energy Production in a Water Supply Network. Water Resources Management, 30(4), 1533-1547. doi:10.1007/s11269-016-1238-5Sanchis, R., Díaz-Madroñero, M., López-Jiménez, P. A., & Pérez-Sánchez, M. (2019). Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs. Water, 11(12), 2432. doi:10.3390/w11122432Alonso Campos, J. C., Jiménez-Bello, M. A., & Martínez Alzamora, F. (2020). Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms. Agricultural Water Management, 227, 105857. doi:10.1016/j.agwat.2019.105857Controlo Ativo de Perdas de Água. Lisboa: EPAL–Empresa Portuguesa das Águas Livres http://www.epal.pt/EPAL/docs/default-source/epal/publica%C3%A7%C3%B5es-t%C3%A9cnicas/controlo-ativo-de-perdas-de-%C3%A1gua.pdf?sfvrsn=30Ndirangu, N., Ng’ang’a, J., Chege, A., de Blois, R.-J., & Mels, A. (2013). Local solutions in Non-Revenue Water management through North–South Water Operator Partnerships: the case of Nakuru. Water Policy, 15(S2), 137-164. doi:10.2166/wp.2013.117Romero, L., Pérez-Sánchez, M., & Amparo López-Jiménez, P. (2017). Improvement of sustainability indicators when traditional water management changes: a case study in Alicante (Spain). AIMS Environmental Science, 4(3), 502-522. doi:10.3934/environsci.2017.3.50