http://yetl.yabesh.ir/yetl1/handle/yetl/19018 2026-04-08T11:22:48Z 2026-04-08T11:22:48Z Yutaro Onuki Yurina Otaki http://yetl.yabesh.ir/yetl1/handle/yetl/4309099 2026-02-16T21:22:01Z 2025-01-01T00:00:00Z

Managing Peak Water Demand Behavior through Dynamic Tariffs Yutaro Onuki; Yurina Otaki Water supply pipe network infrastructure and pumps are designed to meet peak water demand. A 4-month dynamic tariff intervention was conducted to manage peak demand and reduce costs for sustainable waterworks. Dynamic tariffs in power utilities have been examined, but this study is the first to demonstrate them in waterworks. Water smart meters were installed in 1,890 households, and they measured hourly water consumption. Water use over approximately 12 months—encompassing 4 months each before, during, and after the intervention—was analyzed. This tariff system featured relatively higher rates during peak hours and lower rates during off-peak hours. Consumers supplied from one distribution reservoir were subject to these dynamic tariffs (pricing group), while those supplied from another reservoir continued with the usual tariffs (control group). We analyzed hourly water distribution amounts to assess aggregated water usage across multiple consumers. The findings indicate that dynamic tariffs effectively shifted peak water usage. Specifically, off-peak water use significantly increased at 0500 and 2300, while peak water use decreased at 0700. Notably, the increased water use at 2300 persisted even after the intervention ended. In addition, the analysis showed that the increased off-peak water usage at 0500 and 2300 was consistent across all days. The results demonstrate that dynamic tariffs can influence water-usage behavior throughout the week, providing a viable strategy to manage water demand and reduce strain on water distribution systems during peak times. This study highlights the potential of dynamic pricing as an effective tool for waterwork utilities to manage consumption patterns and optimize resource usage. This study demonstrates how water utilities can manage peak-time water demand by implementing higher tariffs when usage is high (e.g., morning and evening) and lower tariffs during off-peak hours (e.g., night/early morning and midday). Water smart meters, electronic devices that can record hourly water consumption digitally, installed in 1,890 households revealed shifts in water consumption patterns. Specifically, usage increased during off-peak hours (0500 and 2300) and decreased during peak hours (0700) in conjunction with the tariff system. This approach could help water utilities reduce operating costs by preventing them from overinvesting in facilities to meet peak demand and make the water distribution system more efficient. Furthermore, by leveling demand peaks, the proposed strategy can mitigate challenges in the water supply network, particularly when meeting peak demand is difficult. For consumers, this will include an increase in water tariffs and a new pricing system incorporating a mechanism where beneficiaries bear the cost. The findings suggest that dynamic pricing can effectively improve demand management and ensure sustainable water supply management in communities. This research provides a practical framework for water utilities to implement time-based pricing strategies for more efficient water use.

2025-01-01T00:00:00Z Laura L. Lopez Jakobus E. van Zyl Piaras A. Kelly http://yetl.yabesh.ir/yetl1/handle/yetl/4309098 2026-02-16T21:22:00Z 2025-01-01T00:00:00Z

Conceptual Framework for Leak Development in Water Distribution Systems Laura L. Lopez; Jakobus E. van Zyl; Piaras A. Kelly Water distribution systems, like all infrastructure, are subject to deterioration and failure, but their buried nature makes continuous condition monitoring impractical. This means that the pipe failure rate is often the only measure of pipe condition. However, most failures occur at the end of a long deterioration process that is driven by a range of factors. The aim of this paper is to propose a conceptual framework that describes the development of different failure types from weak point initiation to the point where the leak flow rate is large enough to be discovered. The paper discusses factors and mechanisms that influence the strength of and loads on pipes, and how these evolve over time. This is combined with a description of the factors affecting the leakage flow rate. The concept of a strength index is defined as the pressure at which a leak will become discoverable and is used to present the development of weak points and leaks on the same scale as the system pressure load. Finally, it is demonstrated how the development of leaks in a typical district metered area (DMA) can be presented using the conceptual framework. The paper provides a framework for the analysis of water distribution system condition over time that can be combined with local knowledge of a system (pipe materials, age, operational practices, and so on), information about failures (causes, types, and dimensions) and specific investigations to better understand the current state of the network.

2025-01-01T00:00:00Z Kang Yang Bin Shi http://yetl.yabesh.ir/yetl1/handle/yetl/4309097 2026-02-16T21:21:58Z 2025-01-01T00:00:00Z

Water Pressure Prediction in a Water Supply Network Using the Informer Framework Kang Yang; Bin Shi Water pressure scheduling is important for the normal operation of urban water supply networks, and water pressure prediction provides the basis and guidance for water pressure scheduling. Informer, as a deep learning model proposed in recent years, brings a new approach to the study of water pressure prediction. In this study, Informer is used to model the water pressure data of the water supply network to achieve long-term water pressure prediction. In order to address the challenge of hyperparameter optimization in deep learning, the Bayesian and HyperBand (BOHB) hyperparameter optimization algorithm, combining BO and HB, is utilized to construct a water pressure prediction framework for automatic hyperparameter optimization with BOHB-Informer. Through experiments on real urban water supply network cases, Informer has 22.5%∼43% lower error than the recurrent neural network (RNN) model in long-term water pressure prediction, and the prediction error of the Informer model optimized by the BOHB-Informer framework is further reduced by 23.7%∼34.1%. The experimental results demonstrate that the framework exhibits excellent parameter tuning performance and prediction accuracy. It can be a valuable auxiliary guidance tool for water pressure scheduling in water supply networks.

2025-01-01T00:00:00Z Ramiz Beig Zali Milad Latifi Akbar A. Javadi Raziyeh Farmani http://yetl.yabesh.ir/yetl1/handle/yetl/4309096 2026-02-16T21:21:56Z 2025-01-01T00:00:00Z

A Weighted Likelihood Ensemble Approach for Failure Prediction of Water Pipes Ramiz Beig Zali; Milad Latifi; Akbar A. Javadi; Raziyeh Farmani This paper presents a novel weighted likelihood ensemble approach for predicting pipe failures in water distribution networks (WDNs). The proposed method leverages ensemble modeling, specifically stacking, to enhance prediction capability. The study utilizes a data set of water pipe failures from 2006 to 2017, segmented into different time intervals. Various classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB), are employed to predict failures within these segments. These individual models are then combined to create ensemble models. The results show that the stacked models consistently outperform the models that use the training data set as a whole. Along with traditional evaluation metrics, practical assessments are conducted, considering different percentages of pipes for replacement. These evaluations align with tactical and strategic maintenance plans. Remarkably, the most significant improvements are observed in models with lower replacement percentages. The novel aspect of this approach lies in assigning weights to prediction results from different models, each utilizing distinct time segments of data. By developing a meta-model with linear regression based on weighted likelihoods of pipe failures, this method provides valuable insights for asset managers and decision makers. It aids in prioritizing pipe rehabilitation programs, with the potential for further refinement as new failure data becomes available.

2025-01-01T00:00:00Z