Micro-component survey of residential water consumption in Hanoi

2013 ◽  
Vol 13 (2) ◽  
pp. 469-478 ◽  
Author(s):  
Y. Otaki ◽  
M. Otaki ◽  
P. N. Bao ◽  
T. T. V. Nga ◽  
T. Aramaki
Keyword(s):  
Water Consumption ◽  
Basic Unit ◽  
Water Usage ◽  
Total Water Consumption ◽  
Domestic Use ◽  
Residential Water Consumption ◽  
Economic Perspectives ◽  
End Use ◽  
Policy Direction ◽  
Efficient Water Use

Daily total water consumption per capita has been used as a basic unit for the future planning of water supply for domestic use. However, for innovative water utilization designs that consider various scenarios, including the effects of policy direction and global warming, and more strategic and efficient water use, it is absolutely essential to consider water usage divided by residential activities, such as toilet flushing, cooking, clothes washing, and bathing. We collected micro-component data by direct measurement from each household outlet, and developed small accumulative meters. Measurements were conducted at 56 households for 2 months in Hanoi, Vietnam, and the average consumption was 18.6 L/p/d for toilet, 16.2 L/p/d for laundry, 10.4 L/p/d for bath, and 15.7 L/p/d for kitchen. We then analyzed the representative values and the distribution of water consumption for every usage from social and economic perspectives. In addition, we compared the results in Hanoi with those in Chiang Mai, Thailand, where we investigated water consumption a few years ago, and their value seemed similar except for bathroom use, but the substance was different. One distinct outcome of our investigation was the recognition of the cultural and methodological challenges to end-use assessment of water consumption in modernizing Asian communities.

Analyzing the Effect of Mandatory Water Restrictions on Water Usage

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Trey Dronyk-Trosper ◽  
Brandli Stitzel
Keyword(s):  
Water Use ◽  
Water Consumption ◽  
Water Rights ◽  
Water Usage ◽  
Total Water ◽  
Water Restriction ◽  
Way Of Life ◽  
Total Water Consumption ◽  
Water Restrictions

AbstractAs water rights and water usage become an ever more important part of municipalities’ and states’ way of life, it becomes important to understand what policies can be effective for encouraging conservation of water. One method that has been employed at various times and throughout numerous communities is to limit outdoor watering days. We use a dataset with over 3 million property-month observations during the 2007–2015 period in Norman, Oklahoma, to identify whether the periodic implementation of mandatory water restrictions reduces water usage. Our data allow us to exploit variance in the timing of these water restriction programs. Our findings indicate that this policy reduces water consumption by 0.7 % of total water consumption. Additionally, we use home assessment prices to identify heterogeneity in this response, finding that high priced homes are more responsive to water use restrictions.

Water Utilization in Data Center Infrastructure

2010 ◽  
Author(s):  
Ratnesh Sharma ◽  
Rocky Shih ◽  
Alan McReynolds ◽  
Cullen Bash ◽  
Chandrakant Patel ◽  
...  
Keyword(s):  
Power Generation ◽  
Water Quality ◽  
Water Consumption ◽  
Data Center ◽  
Data Centers ◽  
Cooling Towers ◽  
Power Demand ◽  
Water Usage ◽  
Water Treatment Plants ◽  
End Use

Fresh water is one of the few resources which is scarce and has no replacement; it is also closely coupled to energy consumption. Fresh water usage for power generation and other cooling applications is well known and accounts for 40% of total freshwater withdrawal in the U. S[1]. A significant amount of energy is embedded in the consumption of water for conveyance, treatment and distribution of water. Waste water treatment plants also consume a significant amount of energy. For example, water distribution systems and water treatment plants consume 1.3MWh and 0.5MWh[2], respectively, for every million gallons of water processed. Water consumption in data centers is often overlooked due to low cost impact compared to energy and other consumables. With the current trend towards local onsite generation[3], the role of water in data centers is more crucial than ever. Apart from actual water consumption, the impact of embedded energy in water is only beginning to be considered in water end-use analyses conducted by major utilities[4]. From a data center end-use perspective, water usage can be characterized as direct, for cooling tower operation, and indirect, for power generation to operate the IT equipment and cooling infrastructure[5]. In the past, authors have proposed and implemented metrics to evaluate direct and indirect water usage using an energy-based metric. These metrics allow assessment of water consumption at various power consumption levels in the IT infrastructure and enable comparison with other energy efficiency metrics within a data center or among several data centers[6]. Water consumption in data centers is a function of power demand, outside air temperature and water quality. While power demand affects both direct and indirect water consumption, water quality and outside air conditions affect direct water consumption. Water from data center infrastructure is directly discharged in various forms such as water vapor and effluent from cooling towers. Classification of direct water consumption is one of the first steps towards optimization of water usage. Subsequently, data center processes can be managed to reduce water intake and discharge. In this paper, we analyze water consumption from data center cooling towers and propose techniques to reuse and reduce water in the data center.

Developing a demand model integrating end uses of water (DMEUW): structure and process of integration

2015 ◽  
Vol 71 (4) ◽  
pp. 529-537 ◽  
Author(s):  
R. C. Sarker ◽  
S. Gato-Trinidad
Keyword(s):  
Water Consumption ◽  
Water Conservation ◽  
Water Demand ◽  
Daily Rainfall ◽  
Demand Model ◽  
Data Set ◽  
Residential Water Consumption ◽  
End Use ◽  
Hypertext Markup Language ◽  
End Uses

The process of developing an integrated water demand model integrating end uses of water has been presented. The model estimates and forecasts average daily water demand based on the end-use pattern and trend of residential water consumption, daily rainfall and temperature, water restrictions and water conservation programmes. The end-use model uses the latest end-use data set collected from Yarra Valley Water, Australia. A computer interface has also been developed using hypertext markup language and hypertext pre-processor. The developed model can be used by water authorities and water resource planners in forecasting water demand and by household owners in determining household water consumption.

scholarly journals What does rebounding water use look like? An examination of post-drought and post-flood water end-use demand in Queensland, Australia

2014 ◽  
Vol 14 (4) ◽  
pp. 561-568 ◽  
Author(s):  
C. D. Beal ◽  
A. Makki ◽  
R. A. Stewart
Keyword(s):  
Water Use ◽  
Water Consumption ◽  
Demand Management ◽  
Management Policy ◽  
Dry Period ◽  
Residential Water Consumption ◽  
End Use ◽  
Capacity Data ◽  
Residential Water

Rebounding water use behaviour has been observed in communities that have experienced plentiful water supply following a very dry period. However, the drivers of such rebounds in water consumption are varied and not well understood. Knowledge of such drivers can greatly assist managers towards proactive demand management, modelling and timely promotion of water efficient behaviours. Total and end-use residential water consumption has been tracked in South East Queensland, Australia for a sample of up to 252 homes in post-drought conditions (dam supplies growing but water restrictions continued, changed water use behaviours still ‘fresh’), and during and post-flooding conditions (eased restrictions, 100% dam capacity). Data on end-use water consumption trends using nearly 3 years of residential water end-use data have revealed several interesting patterns of consumption such as a delayed return to pre-drought use, the influence of climate and end-use specific rebounds (e.g. indoor versus outdoor use). The end-use data have helped to identify the drivers of rebounding water consumption which appear to include environmental cues (rainfall, temperature), social cues (e.g. government encouraging consumers to turn on tap) and a gradual general reduction in conservative water use behaviours. The paper concludes with a discussion of how this knowledge can be used to inform long-term demand management policy, particularly in variable climates.

scholarly journals Characterizing Water Use at Mosques in Abu Dhabi

2020 ◽  
Author(s):  
Richard J. Marinshaw ◽  
Hazem Qawasmeh
Keyword(s):  
Water Use ◽  
Water Consumption ◽  
The Other ◽  
Abu Dhabi ◽  
Total Water ◽  
Total Water Consumption ◽  
End Use ◽  
Ritual Cleansing ◽  
End Uses

In areas where Muslims constitute much of the population, mosques can account for a significant portion of overall water consumption. Among the various uses of water at mosques, ablution (i.e., ritual cleansing) is generally assumed to be the largest, by far. As part of an initiative to reduce water consumption at mosques in Abu Dhabi, we collected data on ablution and other end uses for water from hundreds of mosques in and around Abu Dhabi City. This paper takes a closer look at how water is used at mosques in Abu Dhabi and presents a set of water use profiles that provide a breakdown of mosque water consumption by end use. The results of this research indicate that cleaning the mosque (primarily the floors) and some of the other non-ablution end uses at mosques can account for a significant portion of the total water consumption and significantly more than was anticipated or has been found in other countries.

Understanding urban residential end uses of water

2011 ◽  
Vol 64 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Shirley Gato-Trinidad ◽  
Niranjali Jayasuriya ◽  
Peter Roberts
Keyword(s):  
Water Consumption ◽  
Water Conservation ◽  
Water Usage ◽  
Single Family ◽  
Washing Machines ◽  
Consumption Data ◽  
Household Water ◽  
Family Home ◽  
End Use ◽  
End Uses

The ‘end use’ of water is a breakdown of the total household water usage such as water used for toilets, showers, washing machines, taps, lawn watering, etc. Understanding end uses of water will enable water planners, water authorities and household owners determine where water is used/wasted, how much and how often. This paper describes the end uses of water from a number of single-family homes in Greater Melbourne, Australia. The study involves the analysis of water consumption data recorded at 5-s intervals from logged households collected by Yarra Valley Water in Melbourne in 2004. The study determines how much water is used for outdoor and indoor purposes in a single-family home in Melbourne and compares the water usage during winter and summer. Hourly patterns of major end uses of water are also developed. The aim of this study is to improve the understanding of the end uses of water and to assist where to focus water conservation efforts that would be most effective financially and environmentally, and be acceptable to everyone.

ANN versus SARIMA models in forecasting residential water consumption in Tunisia

2013 ◽  
Vol 3 (3) ◽  
pp. 330-340 ◽  
Author(s):  
Maamar Sebri
Keyword(s):  
Neural Networks ◽  
Water Consumption ◽  
Water Demand ◽  
Total Water ◽  
Forecasting Accuracy ◽  
The Neural Network ◽  
Household Water ◽  
Residential Water Consumption ◽  
End Use ◽  
Total Water Demand

Water scarcity and increasing water demand, especially for residential end-use, are major challenges facing Tunisia. The need to accurately forecast water consumption is useful for the planning and management of this natural resource. In the current study, quarterly time series of household water consumption in Tunisia was forecast using a comparative analysis between the traditional Box–Jenkins method and an artificial neural networks approach. In particular, an attempt was made to test the effectiveness of data preprocessing, such as detrending and deseasonalization, on the accuracy of neural networks forecasting. Results indicate that the traditional Box–Jenkins method outperforms neural networks estimated on raw, detrended, or deseasonalized data in terms of forecasting accuracy. However, forecasts provided by the neural network model estimated on combined detrended and deseasonalized data are significantly more accurate and much closer to the actual data. This model is therefore selected to forecast future household water consumption in Tunisia. Projection results suggest that by 2025, water demand for residential end-use will represent around 18% of the total water demand of the country.

scholarly journals RANCANG BANGUN PEMBACAAN VOLUME AIR YANG DIKONSUMSI PELANGGAN PDAM MELALUI SMS

Foristek ◽  
2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Jepri Purwantoro ◽  
Tan Suryani Sollu ◽  
Nurhani Amin
Keyword(s):  
Real Time ◽  
Water Flow ◽  
Water Consumption ◽  
Flow Sensor ◽  
Water Usage ◽  
Average Deviation ◽  
Total Water Consumption ◽  
Pulse Output ◽  
Arduino Uno ◽  
Reading System

The PDAM is still using an analog flow meter. For PDAM customers, information on analog flow meters is difficult to access and convert into payment amounts. With an analog reading system, the PDAM officer still records using the manual method of the customer's total water consumption data. From the problems above, we need a digital reading system that can display customer water consumption and payment data that can be accessed by customers. The reading of the volume of water consumed by PDAM customers via SMS is a device designed based on the Arduino Uno microcontroller, equipped with the ability to monitor water usage in real time using SMS media. The reading of the volume of water consumed by PDAM customers uses a water flow sensor that functions to read the flow of water that passes and provides pulse output. Arduino Uno receives pulse output from the water flow sensor which is then converted into a number that shows the number and tariff of water usage of PDAM customers. The LCD displays information on total usage, usage rates, time and date in real time. The SIM800L module sends SMS based on orders received from customers and PDAM officials. The results achieved in this study are the water flow sensor is able to read the amount of water consumption of PDAM customers with an average deviation of 0.034%. Tests show the results of payment conversions are in accordance with the PDAM payment model in Palu City.

scholarly journals Enhancing Residential Water End Use Pattern Recognition Accuracy Using Self-Organizing Maps and K-Means Clustering Techniques: Autoflow v3.1

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1221 ◽  
Author(s):  
Ao Yang ◽  
Hong Zhang ◽  
Rodney Stewart ◽  
Khoi Nguyen
Keyword(s):  
Pattern Recognition ◽  
Water Consumption ◽  
Recognition Accuracy ◽  
Clustering Algorithms ◽  
Self Organizing Maps ◽  
Residential Water Consumption ◽  
End Use ◽  
Use Studies ◽  
Residential Water ◽  
Self Organizing

The aim of residential water end-use studies is to disaggregate water consumption into different water end-use categories (i.e., shower, toilet, etc.). The authors previously developed a beta application software (i.e., Autoflow v2.1) that provides an intelligent platform to autonomously categorize residential water consumption data and generate management analysis reports. However, the Autoflow v2.1 software water end use event recognition accuracy achieved was between 75 to 90%, which leaves room for improvement. In the present study, a new module augmented to the existing procedure improved flow disaggregation accuracy, which resulted in Autoflow v3.1. The new module applied self-organizing maps (SOM) and K-means clustering algorithms for undertaking an initial pre-grouping of water end-use events before the existing pattern recognition procedures were applied (i.e., ANN, HMM, etc.) For validation, a dataset consisting of over 100,000 events from 252 homes in Australia were employed to verify accuracy improvements derived from augmenting the new hybrid SOM and K-means algorithm techniques into the existing Autoflow v2.1 software. The water end use event categorization accuracy ranged from 86 to 94.2% for the enhanced model (Autoflow v3.1), which was a 1.7 to 9% improvement on event categorization.

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