Smart Water Meters Research Articles

Machine-learning and water energy harvesting based wireless water consumption sensing system in buildings

In recent years, smart wireless remote water meters have become popular, but this kind of smart water meter needs an external power supply, and every 100 to 200 installed this type of water meter, needs to set up an independent collector. This kind of water meter is expensive, and the external power supply will increase the energy consumption of the building, which is not conducive to the circuit safety of the old community. In order to reduce the burden of manual meter reading and reduce the building's "carbon emissions". A wireless water consumption sensing system for buildings based on machine learning and water energy collection is designed. Images are collected and recognized by visual sensors to realize the visual reading of water meter data. The system is powered by a 12V micro water generator and transferred to lithium battery energy storage to realize the self-energy supply of the whole system. The lightweight YOLOv5 was used to implement the model training process. Then, on the premise of the small sample data set, the data set was expanded by a data enhancement method which randomly changed the image brightness and hue. Finally, the training model is deployed on MCU through the TensorFlow Lite framework to realize edge AI detection. Experiments show that the size of the lightweight YOLOv5 is reduced by 87.1% and the mAP reaches 92.3%. The system can complete the water meter wireless meter reading and realize the system self-power stably. In addition, the system can be expanded for other meter reading tasks based on visual features.

Decision Support Strategies for Household Water Consumption Behaviors Based on Advanced Recommender Systems

Water is one of the most important factors that can influence human health. Therefore, constant monitoring of water consumption is essential to maintain a balance of water demand. A recommendation system represents a major challenge, but with huge potential for the water industry, providing consumers the most efficient ways to conserve water based on their data collected from smart water meters. This paper proposes a novel recommendation system design architecture that promotes water conservation behavior among residential consumers from urban areas. We analyzed 480,000 data samples from several households with different profiles to generate personalized recommendations for each household and encourage consumers to adopt measures to raise awareness and reduce water consumption. Moreover, data were collected from three different measurement points in the household (cold_sink, hot_sink, and toilet), with a sampling time of 60 s. The proposed recommendation system implements collaborative filtering combined with a set of rules to generate recommendations based on the consumption patterns of similar households. The results are promising, offering personalized feedback that could help change the consumption behavior of households if the recommendations made are followed.

Exploiting Smart Meter Water Consumption Measurements for Human Activity Event Recognition

Human activity event recognition (HAER) within a residence is a topic of significant interest in the field of ambient assisted living (AAL). Commonly, various sensors are installed within a residence to enable the monitoring of people. This work presents a new approach for HAER within a residence by (re-)using measurements from commercial smart water meters. Our approach is based on the assumption that changes in water flow within a residence, specifically the transition from no flow to flow above a certain threshold, indicate human activity. Using a separate, labeled evaluation data set from three households that was collected under controlled/laboratory-like conditions, we assess the performance of our HAER method. Our results showed that the approach has a high precision (0.86) and recall (1.00). Within this work, we further recorded a new open data set of water consumption data in 17 German households with a median sample rate of 0.083¯ Hz to demonstrate that water flow data are sufficient to detect activity events within a regular daily routine. Overall, this article demonstrates that smart water meter data can be effectively used for HAER within a residence.

Nudging and subsidising farmers to foster smart water meter adoption

Abstract We use a discrete choice experiment with treatments to test if voluntary adoption of smart water meters by French farmers can be fostered by (i) a collective conditional subsidy offered to farmers who adopt a smart meter only if the rate of adoption in their geographic area is sufficiently high and (ii) informational nudges. Using a sample of 1,272 farmers, we find contrasted results regarding our nudges, but we show that a conditional subsidy is an effective tool to foster adoption of smart meters. Interestingly, the willingness to pay for the conditional subsidy is equal to the subsidy amount and independent of the collective adoption threshold.

A Non-Intrusive Load Decomposition Method Based on Generative Adversarial Networks and Asynchronous Federated Learning

The non-intrusive load decomposition method is an essential approach of appreciating the water requirements of consumers. Traditional techniques of load breakdown have a number of drawbacks, including a poor level of water identification precision. In order to do this, a model for load decomposition based on generative adversarial networks has been developed. By applying a convolutional autoencoder to the total power noise, the generative network constructs the power breakdown sequence of the given home appliance. The discriminant network is then utilized to determine whether or not the created sequence is legitimate. In order to achieve a more natural and authentic sequence, the participants compete against one another. In light of the shortcomings of the centralized model training method, the network model implementation scheme based on asynchronous federated learning is utilized to replace the traditional convolution in order to realize the model’s lightweight nature. This allows the model to be applied to terminal devices such as smart water meters, and it is carried out in a cloud-edge collaborative manner. In addition, the model is carried out in a manner that is collaborative between the cloud and the edge. Training not only eases the strain of communication transmission but also protects the privacy of users and the integrity of their data.

Designing smart pulse flow meters using diversion analysis

<span lang="EN-US">The operation of modern housing infrastructure is characterized by a constant increase in the cost of the limited resources used. This necessitates the priority implementation in the concept of a smart home of elements aimed at resource saving and their rational management. The study provides an overview of the implementation architectures of the internet of things (IoT) concept in the construction of home automation systems and the requirements they impose on the implementation of smart primary meters of controlled physical quantities. Based on a diversion analysis, a promising smart water meter was developed. The prototype is ergonomic and has a structural form factor convenient for further integration. The designed model of the electronic module of the water flow monitoring system implements, in addition to typical tasks, additional functionality: transfer of recorded indicators and technical information to the cloud storage, warning the user about an emergency situation, accumulation of current data in non-volatile memory. It is possible to use the accumulated statistics for training the predictive analysis module. The proposed architecture option will allow creating energy-efficient elements of home automation systems in the future.</span>

Smartening up: User experience with smart water metering infrastructure in an African city

Smartening up: User experience with smart water metering infrastructure in an African city

An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements

This study proposes a modular water monitoring IoT system that enables quantitative and qualitative measuring of water in terms of an upgraded version of the water infrastructure to sustain operational reliability. The proposed method could be used in urban and rural areas for consumption and quality monitoring, or eventually scaled up to a contemporary water infrastructure enabling water providers and/or decision makers (i.e., governmental authorities, global water organization, etc.) to supervise and drive optimal decisions in challenging times. The inherent resilience and agility that the proposed system presents, along with the maturity of IoT communications and infrastructure, can lay the foundation for a robust smart water metering solution. Introducing a modular system can also allow for optimal consumer profiling while alleviating the upfront adoption cost by providers, environmental stewardship and an optimal response to emergencies. The provided system addresses the urbanization and technological gap in the smart water metering domain by presenting a modular IoT architecture with consumption and quality meters, along with machine learning capabilities to facilitate smart billing and user profiling.

Smart Water Meter Based on Deep Neural Network and Undersampling for PWNC Detection

Having become aware of how limited all natural resources are, the water leakage problem in piping systems has become a stimulating topic. This problem increased over the last few years even though innovative tools and techniques appeared in the literature and in the consumer market. Identifying water leaks at the nearest point, the household level, is still an unsolved problem because most water meters are mechanical and, therefore, cannot detect leaks. While the issue is not important for water service providers since consumption is charged to the user, the resolution is crucial due to the increasingly relevant concern of saving natural resources. The detection of small but continuous leaks of drinking water in domestic systems is addressed in this work. Machine learning approaches enabled image processing techniques also in uncontrolled environments, overcoming the classical methods but introducing new challenges like power consumption. Using a combination of Convolutional Neural Networks (CNNs) and Long Short–Term Memory Networks (LSTM) within an adaptive under–sampling strategy, it is possible to process the images captured from the mechanical water meter dial and identify the Period With Null Consumption (PWNC) or the consumption class. The presented solution can classify the water flow into four different classes, and, in the case of absence or small flow, its function becomes to detect leakages. Analyzing images from a mechanical water meter quadrant, it has been possible to identify PWNC and detect small water leakages in the domestic environment under common consumer flow profiles. In addition to the confusion matrices, the synthetic parameters of Sørensen–Dice coefficient (DSC) and Jaccard Index have been utilized and presented to quantify the performance of the proposed DNN. The conducted experiments on static and dynamic water flow demonstrated the applicability of this approach and the possibility of an increase in PWNC identification thanks to the adaptative increase in the sampling time. Moreover, the reduction of sampling time allows for the reduction of computational load and power consumption in embedded scenarios where limited energy is available.

Proposing a Water Distribution Network Solution for an Arid Region Using the Criticality Tool

Water is an essential component of our daily life. Water is delivered to our homes through a water distribution system. Various analyses are required to understand how various elements affect the network. We may run many scenarios on the network and examine the results using the Water Gems software. This software also helps to shorten the time it takes to figure out where a pipe has been damaged, allowing for more effective water saving. The analysis is carried out in order to improve the network’s efficiency. Changes in hydraulic parameters, physical parameters, and other variables can be determined using these analyses. The proposed water distribution network is Nimkhedi, Jalgaon. Criticality analysis is a method that analyzes every potential machine breakdown scenario and evaluates how those failures could impact the network overall. Clarifying what may be done to lower the risk connected with each resource can be done by doing a criticality analysis. A versatile tool to assess and determine the most important components of a water distribution network is provided by criticality to the user. The Smart Water Metering System is crucial for consumer convenience and water management.

Household flow detection using FEAT (flow estimating accelerometer-thermometer) device

The use of IoT devices in water end use disaggregation verification is an emerging field which offers benefits over conventional approaches, in terms of cost, accuracy and scalability. Having reliably disaggregated water appliance consumption data will enable smart water meter data to be used in household water conservation approaches and for understanding water consumption behaviours. The FEAT device provides a low cost, easily applied and scalable solution that is demonstrated to work even for very low flow conditions of 0.03 l/s. The FEAT device is a combination of a battery, Wi-fi board and MPU6050 sensors providing multi-modal accelerometer and thermometer data. The study places 7 of these FEAT devices onto hot and cold water pipes leading to a shower, which is operated 4 times in a high flow situation, 0.13 l/s, and 4 times in a low flow situation, 0.03 l/s. The data is then analysed and compared with a flow logger to determine if the FEAT device can detect when a domestic appliance is using water. There are limiting cases where the level of noise or external interference limits distorts the data, obscuring the distinguishable peaks in the data due to the similarity of the values. By using high and low pass filtering methods it was possible to enhance the peaks but there are still situations where peaks cannot be detected: for example, if a rigid pipe is not able to vibrate easily or if a hot water boiler is not triggered due to the low flow rate. However, the results show it should be possible to overcome these limiting cases, as it is much less likely for both the vibration and temperature data to be adversely affected by noise or external influences simultaneously, therefore decreasing the effect of noise and external influences. In conclusion, this research paper demonstrates that FEAT devices are a low cost, easily applied and scalable solution for detecting flow. By using high and low pass filtering, placing sensors on freely moving pipes and through the use of multi-modal verification, the FEAT device is shown to work on both metal and plastic pipes even in the lowest flow situations of 0.03 l/s. Therefore the FEAT device is a suitable solution for appliance identification in disaggregation verification datasets.

Household willingness to pay for a smart water metering and monitoring system: The case of South Korea

This paper attempts to appraise the public value of expanding the introduction of a smart water metering and monitoring system (SWMMS) nationwide by adopting a contingent valuation approach of asking about a consumer's preference for a good using a survey of people. Randomly chosen 1000 households were interviewed using the one-and-one-half-bounded question. The monthly public value was obtained as KRW 862 (USD 0.77) per household, equivalent to 6.6% of a water bill. The monthly related cost was calculated as KRW 425 (USD 0.38) per household. Thus, the introduction of an SWMMS would be socially profitable.

Is smart water meter temporal resolution a limiting factor to residential water end-use classification? A quantitative experimental analysis

Water monitoring in households provides occupants and utilities with key information to support water conservation and efficiency in the residential sector. High costs, intrusiveness, and practical complexity limit appliance-level monitoring via sub-meters on every water-consuming end use in households. Non-intrusive machine learning methods have emerged as promising techniques to analyze observed data collected by a single meter at the inlet of the house and estimate the disaggregated contribution of each water end use. While fine temporal resolution data allow for more accurate end-use disaggregation, there is an inevitable increase in the amount of data that needs to be stored and analyzed. To explore this tradeoff and advance previous studies based on synthetic data, we first collected 1 s resolution indoor water use data from a residential single-point smart water metering system installed at a four-person household, as well as ground-truth end-use labels based on a water diary recorded over a 4-week study period. Second, we trained a supervised machine learning model (random forest classifier) to classify six water end-use categories across different temporal resolutions and two different model calibration scenarios. Finally, we evaluated the results based on three different performance metrics (micro, weighted, and macro F1 scores). Our findings show that data collected at 1- to 5-s intervals allow for better end-use classification (weighted F-score higher than 0.85), particularly for toilet events; however, certain water end uses (e.g., shower and washing machine events) can still be predicted with acceptable accuracy even at coarser resolutions, up to 1 min, provided that these end-use categories are well represented in the training dataset. Overall, our study provides insights for further water sustainability research and widespread deployment of smart water meters.

The role of knowledge about water consumption in the context of intentions to use IoT water metrics

Technological progress can contribute to a more conscious and sustainable consumption of water. This is especially important in the context of dwindling resources and climate change. The objective of the study is to investigate how consumers’ perceived knowledge on water usage influences their intention to adopt smart (IoT-based) water meters, which deliver very precise data on the amount of water used in a household. We hypothesized that perceived knowledge on personal water usage exerts a direct and indirect influence on the intention to adopt a smart water meter. For the mediators, we used the intention to save water and variables derived from Value-Belief-Norm theory: awareness of consequences, ascription of responsibility and personal norm. We verified the hypotheses by applying structural equation modelling to a sample of 532 respondents. We found that perceived knowledge on water usage directly influences the intention to adopt a smart water meter, and that all considered variables worked to mediate the relationship between perceived knowledge and intention. Thus, based on our results perceived knowledge plays an important role on the relationship between values-beliefs-norms and intentions to apply smart water meters, which can be used for future research in order to reduce household water consumption by increasing the probability of installing smart water meters.

IoT approach towards smart water usage

This paper presents the design and implementation of intelligent buildings for the monitoring of building parameters, which includes both the required hardware and the associated software. For implementation purposes, the paper focuses on technical descriptions of the steps required to successfully implement IoT architecture while providing applications for the implementation of smart water meters and providing data analysis. The main goal of such a system is to provide the user with transparent data in real-time. Looking at consumption overtime on different platforms is essential, as it can change habits and patterns of water consumption, reducing costs for users and the overall operating system. By increasing distribution efficiency and maintaining resource sustainability, smart water distribution can be achieved.

Urban household water usage in adaptation to climate change: Evidence from China

While it has been concluded that climate change poses a significant threat to worldwide supply of freshwater resources, it is unclear if and how demand for water would also be affected. To fill this knowledge gap, we leverage on ‘big data’ collected using smart water meters from over 40,000 Chinese urban households, spanning nine years and ten provinces to examine the relationship between daily household water usage and climate variability. At the baseline, we find that municipal water is not only a coping mechanism for heat, but its usage is accelerated during heatwave events. Heterogeneity analyses reveal that households from lower-valued properties are more likely to substitute water for electricity to counter heat. Importantly, we find evidence of adaptation behaviors where over time, households are using increasingly more water to cope with high-temperature days. In all, after feeding our results into climate projection models, it is estimated that household water usage will increase by around 7–44% in the long-term (2080–2099) under emissions scenarios of SSP245 and SSP370. Our findings are especially relevant for water-scarce countries such as China as well as developing countries where water is a cheaper and more accessible resource to cope with heat.

Research on intelligent management of campus water supply system based on mathematical model

The campus water supply system is an important part of the campus utilities. This paper studies the campus water supply management from the water supply data obtained from the smart water meter of a university campus in China. It used the mathematical modeling method to analyze the water consumption characteristics of each functional area of the campus, drew a mind map of the hierarchical relationship, and made a line graph of water consumption in each area with the change of month, established a model between each water meter data, and obtained the hierarchical relationship and model error of the campus water meter. For the leakage of the water transmission network, a leakage rate model was derived to help the school address the leakage problems that exist.

Development of a Deep Learning-Based Prediction Model for Water Consumption at the Household Level

The importance of efficient water resource supply has been acknowledged, and it is essential to predict short-term water consumption in the future. Recently, it has become possible to obtain data on water consumption at the household level through smart water meters. The pattern of these data is nonlinear due to various factors related to human activities, such as holidays and weather. However, it is difficult to accurately predict household water consumption with a nonlinear pattern with the autoregressive integrated moving average (ARIMA) model, a traditional time series prediction model. Thus, this study used a deep learning-based long short-term memory (LSTM) approach to develop a water consumption prediction model for each customer. The proposed model considers several variables to learn nonlinear water consumption patterns. We developed an ARIMA model and an LSTM model in the training dataset for customers with four different water-use types (detached houses, apartment, restaurant, and elementary school). The performances of the two models were evaluated using a test dataset that was not used for model learning. The LSTM model outperformed the ARIMA model in all households (correlation coefficient: mean 89% and root mean square error: mean 5.60 m3). Therefore, it is expected that the proposed model can predict customer-specific water consumption at the household level depending on the type of use.

Smart Water Meters Can Save Lives during the COVID-19 Pandemic

Smart Water Meters Can Save Lives during the COVID-19 Pandemic

Nudging urban residential water conservation through smart metering

Indian cities face increasing risk of water stress. This article emphasises the need for demand side water management. It highlights the determinants and experiences of smart water metering adoption by residential communities in Chennai, India.