Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems

Salvino, Laís Régis; Gomes, Heber Pimentel; Bezerra, Saulo de Tarso Marques

doi:10.1007/s11269-022-03175-4

Cited by 5 publications

(6 citation statements)

References 31 publications

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“…Prior studies have applied machine learning algorithms to simulate the energy use for wastewater treatment plants (Bagherzadeh et al 2021;Das, Kumawat, and Chaturvedi (Antonopoulos and Gianniou 2022;Salvino, Gomes, and Bezerra 2022). Prior studies concluded that the ANN model was useful in simulating the energy efficiency of the water distribution system (Das, Kumawat, and Chaturvedi 2021;Salvino, Gomes, and Bezerra 2022). We also found that the DNN model, an ANN model with multiple hidden layers between the input and output layers, was the most effective machine learning algorithm in predicting the energy use of the water distribution system.…”

Section: Discussionmentioning

confidence: 92%

“…We found no comparative research on predicting energy use for the whole or the conveyance system. Prior studies have applied machine learning algorithms to simulate the energy use for wastewater treatment plants (Bagherzadeh et al 2021;Das, Kumawat, and Chaturvedi (Antonopoulos and Gianniou 2022;Salvino, Gomes, and Bezerra 2022). Prior studies concluded that the ANN model was useful in simulating the energy efficiency of the water distribution system (Das, Kumawat, and Chaturvedi 2021;Salvino, Gomes, and Bezerra 2022).…”

Section: Discussionmentioning

confidence: 99%

“…The California Energy Commission, the primary energy planning agency in California, has been using regression analysis to forecast energy use in agricultural and municipal water pumping (California Energy Commission 2005). Machine learning-based models have also proven to be useful in simulating the energy use for a wastewater treatment plant (Bagherzadeh et al 2021;Das, Kumawat, and Chaturvedi 2021;Li and Tang 2021;Zhang et al 2021) and a distribution system (Salvino, Gomes, and Bezerra 2022). Various studies have applied machine learning algorithms in forecasting water-related energy demand but were limited to forecasting the energy use for a single water facility.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Kondolf

Sandoval-Solís

et al. 2022

Water Resour Manage

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Energy use forecasting is crucial in balancing the electricity supply and demand to reduce the uncertainty inherent in the inter-basin water transfer project. Energy use prediction supports the reliable water-energy supply and encourages cost-effective operation by improving generation scheduling. The objectives are to develop subsequent monthly energy use predictive models for the Mokelumne River Aqueduct in California, US. Partial objectives are to (a) compare the model performance of a baseline model (multiple linear regression (MLR)) to three machine learning-based models (random forest (RF), deep neural network (DNN), support vector regression (SVR)), (b) compare the model performance of the whole system to three subsystems (conveyance, treatment, distribution), and (c) conduct sensitivity analysis. We simulate a total of 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, treatment, distribution) x 4 scenarios (different combinations of independent variables). We concluded that the three machine learning algorithms showed better model performance than the baseline model as they reflected non-linear energy use characteristics for water transfer systems. Among the three machine learning algorithms, DNN models yielded higher model performance than RF and SVR models. Subsystems performed better than the whole system as the models more closely reflected the unique energy use characteristics of the subsystems. The best case was having water supply (t), water supply (t-1), precipitation (t), temperature (t), and population (y) as independent variables. These models can help water and energy utility managers to understand energy performance better and enhance the energy efficiency of their water transfer systems.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Kondolf

Sandoval-Solís

et al. 2022

Water Resour Manage

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“…In [ 15 ], a new artificial neural network (ANN) controller was developed to improve the operation of water distribution systems (WDSs), including in its algorithm the specific energy consumption as a parameter. The technique was applied to control the pressures in an experimental configuration that emulated a WDS with two consumption zones with different topographies.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements

Araújo

Villanueva

Cordula³

et al. 2022

Sensors

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As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving electricity and preserving piping from excess pressure. However, to obtain these benefits, it is necessary to overcome some difficulties such as sensing the pressure of geographically distant consumer units and developing a control logic that is capable of making use of the data from these sensors and, at the same time, a good solution in terms of cost benefit. Therefore, this work has the purpose of developing a pressure monitoring and control system for water supply networks, using the ESP8266 microcontroller to collect data from pressure sensors for the integrated ScadaLTS supervisory system via the REST API. The modeling of the plant was developed using artificial neural networks together with fuzzy pressure control, both designed using the Python language. The proposed method was tested by considering a pumping station and two reference units located in the city of João Pessoa, Brazil, in which there was an excess of pressure in the supply network and low performance from the old controls, during the night period from 12:00 a.m. to 6:00 a.m. The field results estimated 2.9% energy saving in relation to the previous form of control and a guarantee that the pressure in the network was at a healthy level.

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“…In their work, Flores et al [3] proposed a fuzzy logic-based pressure control logic, which proved effective in increasing energy efficiency in a distribution network built in a laboratory with series and parallel pump arrangements. Following a similar path, Salvino et al [4] developed a neural network-based controller to improve the energetic efficiency of water distribution systems, using specific energy consumption (SEC) as a criterion. This kind of pressure control solution developed in the laboratory has proven valid in real water supply systems as was shown by Araújo et al [5], who applied the pressure provided by a fuzzy control in a model based on MLP artificial neural networks before applying it in a real urban WSS.…”

Section: Introductionmentioning

confidence: 99%

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

Alencar,

Fernandes,

Moura Duarte

et al. 2024

Automation

Self Cite

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The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sensors that have the potential to replace traditional physical sensors in order to reduce costs and enhance efficiency. This study explores the implementation of an artificial neural network (ANN) based soft sensor model in a water supply system to predict flow rates within the system. The soft sensor is centered on a Long Short-Term Memory (LSTM) artificial neural network model using Monte Carlo dropout to reduce uncertainty and improve estimation performance. Based on the results of this work, it is concluded that the proposed soft sensor (with Monte Carlo dropout) can predict flow rates more precisely, contributing to the reduction in water losses, as well as cost savings. This approach offers a valuable solution for minimizing water losses and ensuring the efficient use of this vital resource. Regarding the use of soft sensors based on LSTM neural networks with a careful choice of Monte Carlo dropout parameters, when compared to the multilayer perceptron model, the LSTM model with Monte Carlo dropout showed better mean absolute error, root mean square error, and coefficient of determination: 0.2450, 0.3121, and 0.996437 versus 0.2556, 0.3522, and 0.9954. Furthermore, this choice of Monte Carlo dropout parameters allowed us to achieve an LSTM network model capable of reducing uncertainty to 1.8290, keeping the error metrics also at low levels.

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Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems

Cited by 5 publications

References 31 publications

Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

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