Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction

Calibration Procedure for Water Distribution Systems: Comparison among Hydraulic Models

Proper hydraulic simulation models, which are fundamental to analyse a water distribution system, require a calibration procedure. This paper proposes a multi-objective procedure to calibrate water demands and pipe roughness distribution in the context of an ill-posed problem, where the number of measurements is smaller than the number of variables. The proposed methodology consists of a two-steps procedure based on a genetic algorithm. Firstly, several runs of the calibrator are performed and the corresponding pressure and flow-rates values are averaged to overcome the non-uniqueness of the solutions problem. Secondly, the final calibrated model is achieved using the calibrator with the average values of the previous step as the reference condition. Therefore, the procedure enables to obtain physically based hydraulic parameters. Moreover, several hydraulic models are investigated to assess their performance on this optimisation procedure. The considered models are based either on concentrated at nodes or distributed along pipes demands approach, but also either on demand driven or pressure driven approach. Results show the reliability of the final calibrated model in the context of the ill-posed problem. Moreover, it is observed the overall better performance of the pressure driven approach with distributed demand in scarce pressure condition

Burst Detection in Water Distribution Systems: The Issue of Dataset Collection

Developing data-driven models for bursts detection is currently a demanding challenge for efficient and sustainable management of water supply systems. The main limit in the progress of these models lies in the large amount of accurate data required. The aim is to present a methodology for the generation of reliable data, which are fundamental to train anomaly detection models and set alarms. Thus, the results of the proposed methodology is to provide suitable water consumption data. The presented procedure consists of stochastic modelling of water request and hydraulic pipes bursts simulation to yield suitable synthetic time series of flow rates, for instance, inlet flows of district metered areas and small water supply systems. The water request is obtained through the superimposition of different components, such as the daily, the weekly, and the yearly trends jointly with a random normal distributed component based on the consumption mean and variance, and the number of users aggregation. The resulting request is implemented into the hydraulic model of the distribution system, also embedding background leaks and bursts using a pressure-driven approach with both concentrated and distributed demand schemes. This work seeks to close the gap in the field of synthetic generation of drinking water consumption data, by establishing a proper dedicated methodology that aims to support future water smart grids

An Investigation on the Effect of Leakages on the Water Quality Parameters in Distribution Networks

Leakages in distribution networks reach more than 30% of the water supplied, entailing important risks for the water infrastructure with water contamination issues. Therefore, it is necessary to develop new methods to mitigate the amount of water wastes. This study proposes to seek new sources of information that can help for a more sustainable water use. Hence, an analysis of the network is presented, showing the hydraulic behavior during leaks occurrence, placing emphasis on how these events affect and modify water quality parameters, such as water age and chlorine concentration. The study enhances that water quality data can be an effective source of information in the case of leaks, being a possible source of information for future detection systems. In addition, this study proposes to use graph theory on the water network. The results highlight how an analysis of the shortest path between the leak location and the reservoir could provide meaningful information for future detection systems

Assessment of ERA5-Land Data in Medium-Term Drinking Water Demand Modelling with Deep Learning

Drinking water demand modelling and forecasting is a crucial task for sustainable management and planning of water supply systems. Despite many short-term investigations, the medium-term problem needs better exploration, particularly the analysis and assessment of meteorological data for forecasting drinking water demand. This work proposes to analyse the suitability of ERA5-Land reanalysis data as weather input in water demand modelling. A multivariate deep learning model based on the long short-term memory architecture is used in this study over a prediction horizon ranging from seven days to two months. The performance of the model, fed by ground station data and ERA5-Land data, is compared and analysed. Close-to-operative forecasting is then presented using observed data for training and ERA5-Land dataset for testing. The results highlight the reliability of the proposed architecture fed by ERA5-Land data for different time horizons. In particular, the ERA5-Land shows promising performance as input of the multivariate machine learning forecasting model, although some meteorological biases are present, which can be improved, especially in close-to-operative application with bias correction techniques. The proposed study leads to practical implications in the use of regional climate model outputs to support drinking water forecasting for sustainable and efficient management of water distribution systems

Optimal Selection and Monitoring of Nodes Aimed at Supporting Leakages Identification in WDS

Many efforts have been made in recent decades to formulate strategies for improving the efficiency of water distribution systems (WDS), led by the socio-demographic evolution of modern society and the climate change scenario. The improvement of WDS management is a complex task that can be addressed by providing services to maximize revenues while ensuring that the quality standards required by national and international regulations are upheld. These two objectives can be fulfilled by utilizing optimized techniques for the operational and maintenance strategies of WDS. This paper proposes a methodology for assisting engineers in identifying water leakages in WDS, thus providing an effective procedure for ensuring high level hydraulic network functionality. The proposed approach is based on an inverse analysis of measured flow rates and pressure data, and consists of three steps: The analysis of measurements to select the most suitable period for leakage identification, the localization of the best measurement points based on a correlation analysis, and leakage identification with a hybrid optimization that combines the exploration capability of the differential evolution algorithm with the rapid convergence of particle swarm optimization. The proposed procedure is validated on a reference hydraulic network, known as the Apulian network

Short-term hydropower optimization driven by innovative time-adapting econometric model

The ongoing transformation of the electricity market has reshaped the hydropower production paradigm for storage reservoir systems, with a shift from strategies oriented towards maximizing regional energy production to strategies aimed at the revenue maximization of individual systems. Indeed, hydropower producers bid their energy production scheduling 1 day in advance, attempting to align the operational plan with hours where the expected electricity prices are higher. As a result, the accuracy of 1-day ahead prices forecasts has started to play a key role in the short-term optimization of storage reservoir systems. This paper aims to contribute to the topic by presenting a comparative assessment of revenues provided by short-term optimizations driven by two econometric models. Both models are autoregressive time-adapting hourly forecasting models, which exploit the information provided by past values of electricity prices, with one model, referred to as Autoarimax, additionally considering exogenous variables related to electricity demand and production. The benefit of using the innovative Autoarimax model is exemplified in two selected hydropower systems with different storage capacities. The enhanced accuracy of electricity prices forecasting is not constant across the year due to the large uncertainties characterizing the electricity market. Our results also show that the adoption of Autoarimax leads to larger revenues with respect to the use of a standard model, increases that depend strongly on the hydropower system characteristics. Our results may be beneficial for hydropower companies to enhance the expected revenues from storage hydropower systems, especially those characterized by large storage capacity

Optimizing of rehabilitation alternatives for large intermittent water distribution systems

Brentan, B.; Carpitella, S.; Zanfei, A.; Souza, RG.; Menapace, A.; Meirelles, G.; Izquierdo Sebastián, J. (2022). Optimizing of rehabilitation alternatives for large intermittent water distribution systems. Universitat Politècnica de València. 139-142. http://hdl.handle.net/10251/19242113914