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Journal articles on the topic 'Residential water demand'

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Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Tricarico, C., G. de Marinis, R. Gargano, and A. Leopardi. "Peak residential water demand." Proceedings of the Institution of Civil Engineers - Water Management 160, no. 2 (June 2007): 115–21. http://dx.doi.org/10.1680/wama.2007.160.2.115.

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2

Bao, Keyu, Rushikesh Padsala, Daniela Thrän, and Bastian Schröter. "Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model." ISPRS International Journal of Geo-Information 9, no. 11 (October 28, 2020): 642. http://dx.doi.org/10.3390/ijgi9110642.

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Humans’ activities in urban areas put a strain on local water resources. This paper introduces a method to accurately simulate the stress urban water demand in Germany puts on local resources on a single-building level, and scalable to regional levels without loss of detail. The method integrates building geometry, building physics, census, socio-economy and meteorological information to provide a general approach to assessing water demands that also overcome obstacles on data aggregation and processing imposed by data privacy guidelines. Three German counties were used as validation cases to prove the feasibility of the presented approach: on average, per capita water demand and aggregated water demand deviates by less than 7% from real demand data. Scenarios applied to a case region Ludwigsburg in Germany, which takes the increment of water price, aging of the population and the climate change into account, show that the residential water demand has the change of −2%, +7% and −0.4% respectively. The industrial water demand increases by 46% due to the development of economy indicated by GDP per capita. The rise of precipitation and temperature raise the water demand in non-residential buildings (excluding industry) of 1%.

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3

Palencia, Lamberto C. "RESIDENTIAL WATER DEMAND IN METRO MANILA." Journal of the American Water Resources Association 24, no. 2 (April 1988): 275–79. http://dx.doi.org/10.1111/j.1752-1688.1988.tb02984.x.

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4

Lopez-Mayan, Cristina. "Microeconometric Analysis of Residential Water Demand." Environmental and Resource Economics 59, no. 1 (September 5, 2013): 137–66. http://dx.doi.org/10.1007/s10640-013-9721-4.

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5

Gato, Shirley, Niranjali Jayasuriya, and Peter Roberts. "Forecasting Residential Water Demand: Case Study." Journal of Water Resources Planning and Management 133, no. 4 (July 2007): 309–19. http://dx.doi.org/10.1061/(asce)0733-9496(2007)133:4(309).

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6

Hung, Ming-Feng, Bin-Tzong Chie, and Tai-Hsin Huang. "Residential water demand and water waste in Taiwan." Environmental Economics and Policy Studies 19, no. 2 (April 13, 2016): 249–68. http://dx.doi.org/10.1007/s10018-016-0154-5.

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7

Metaxas, S., and E. Charalambous. "Residential price elasticity of demand for water." Water Supply 5, no. 6 (December 1, 2005): 183–88. http://dx.doi.org/10.2166/ws.2005.0063.

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This paper presents an analysis on price elasticity of demand for water as a consequence of price increases. The objective of this research study is to estimate the residential price elasticities of demand for water for different regions, which may have different income levels. The general conclusion is that price elasticity for residential water use is inelastic (i.e. a given percentage of price increase results in a proportionally smaller decrease in quantity demanded) and it varies by consumer class and type of water use. The elasticity is not significantly affected by demographic and other factors.

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8

Lyman, R. Ashley. "Peak and off-peak residential water demand." Water Resources Research 28, no. 9 (September 1992): 2159–67. http://dx.doi.org/10.1029/92wr01082.

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9

Schleich, Joachim, and Thomas Hillenbrand. "Determinants of residential water demand in Germany." Ecological Economics 68, no. 6 (April 2009): 1756–69. http://dx.doi.org/10.1016/j.ecolecon.2008.11.012.

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10

Gargano, Rudy, Carla Tricarico, Giuseppe del Giudice, and Francesco Granata. "A stochastic model for daily residential water demand." Water Supply 16, no. 6 (June 20, 2016): 1753–67. http://dx.doi.org/10.2166/ws.2016.102.

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Residential water demand is a random variable which influences greatly the performance of municipal water distribution systems (WDSs). The water request at network nodes reflects the behavior of the residential users, and a proper characterization of their water use habits is vital for the hydraulic system modeling. This study presents a stochastic approach for the characterization of the daily residential water use. The proposed methodology considers a unique probabilistic distribution – mixed distribution – for any time during the day, and thus for any entity of the water demanded by the users. This distribution is obtained by the merging of two cumulative distribution functions taking into account the spike of the cumulative frequencies for the null requests. The methodology has been tested on three real water distribution networks, where the water use habits are different. Experimental relations are given to estimate the parameters of the proposed stochastic model in relation to the users number and to the average daily trend. Numerical examples for a practical application have shown the effectiveness of the proposed approach in order to generate the time series for the residential water demand.

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11

Suárez-Varela, Marta. "Modeling residential water demand: An approach based on household demand systems." Journal of Environmental Management 261 (May 2020): 109921. http://dx.doi.org/10.1016/j.jenvman.2019.109921.

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12

Ghimire, Monika, Tracy A. Boyer, Chanjin Chung, and Justin Q. Moss. "Estimation of Residential Water Demand under Uniform Volumetric Water Pricing." Journal of Water Resources Planning and Management 142, no. 2 (February 2016): 04015054. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000580.

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13

Zapata, Oscar. "More Water Please, It's Getting Hot! The Effect of Climate on Residential Water Demand." Water Economics and Policy 01, no. 03 (September 2015): 1550007. http://dx.doi.org/10.1142/s2382624x15500071.

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Climate change is expected to alter the supply and demand for water in the residential sector. Existing studies exploit the differences in climate across seasons mostly in North America and Europe, and identify changes in consumption levels attributed only to households' short-term responses. The results from models that simulate household consumption of water are sensitive to the parameters that govern the behavior of climate variables and household responses in the upcoming decades, and fail to consider short-term determinants of water consumption. The findings in the literature suggest an inexistent or small effect of climate on residential water demand. This paper studies the relationship between climate conditions and residential water consumption that corresponds to households' long-term adaptation to climate, while controlling for the effect of short-term determinants of water demand. I take advantage of the geographic variation in climate conditions across municipalities of Ecuador to identify the effect of temperature, precipitation and humidity on water demand. I adopt average prices and an IV technique to address the endogeneity problem between water prices and quantities that arise from the use of increasing-block water tariffs. I find a large and significant effect of temperature on residential water demand, whereas precipitation and humidity have a small effect. Temperature also has a stronger effect on water demand among low-income households.

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14

Lombardi, Francesco, Guglielmo Silvagni, Piero Sirini, Riccardo Spagnuolo, and Fabio Volpi. "Daily water demand." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 13, no. 5 (October 1, 2018): 1. http://dx.doi.org/10.4136/ambi-agua.2239.

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This paper develops a model to characterize the demand for domestic water based on its end users' usage habits. The use of individual residential appliances (bathroom sink, toilet, shower, bath, etc.) is interpreted using a probabilistic approach. The paper also applies the model to the distribution network of the municipality of Sparanise, a small city in the province of Caserta, Italy. The results of this application are compared to the real output of the city's actual water reservoir. Flow variability during the day was successfully modelled. A comparison of the simulated and recorded data on a daily level indicates the proper adjustment of the volume distribution; the peak flow rates were also comparable. The model could be a useful tool for analyzing domestic water consumption, especially in the design and management of water distribution networks. Use of the model would particularly aid the Integrated Urban Water Management Operator both in optimizing the operating pressures in the various districts’ networks and in predicting domestic water consumption when drafting its water balance documents.

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15

Al-Mohannadi, Hassan I., Chris O. Hunt, and Adrian P. Wood. "Controlling Residential Water Demand in Qatar: An Assessment." AMBIO: A Journal of the Human Environment 32, no. 5 (August 2003): 362–66. http://dx.doi.org/10.1579/0044-7447-32.5.362.

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16

Rathnayaka, Kumudu, Hector Malano, Shiroma Maheepala, Biju George, Bandara Nawarathna, Meenakshi Arora, and Peter Roberts. "Seasonal Demand Dynamics of Residential Water End-Uses." Water 7, no. 12 (January 7, 2015): 202–16. http://dx.doi.org/10.3390/w7010202.

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17

Ali, Elhadi Ramadan, Mabroka Mohamed Daw, and Mohd Ekhwan Toriman. "Determinants of urban residential water demand in Libya." International Journal of Innovation and Sustainable Development 15, no. 3 (2021): 261. http://dx.doi.org/10.1504/ijisd.2021.10038213.

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18

Daw, Mabroka Mohamed, Elhadi Ramadan Ali, and Mohd Ekhwan Toriman. "Determinants of urban residential water demand in Libya." International Journal of Innovation and Sustainable Development 15, no. 3 (2021): 261. http://dx.doi.org/10.1504/ijisd.2021.115963.

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19

Lee, Juneseok, and Stephanie A. Tanverakul. "Price elasticity of residential water demand in California." Journal of Water Supply: Research and Technology-Aqua 64, no. 2 (September 24, 2014): 211–18. http://dx.doi.org/10.2166/aqua.2014.082.

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20

Worthington, Andrew C., and Mark Hoffman. "AN EMPIRICAL SURVEY OF RESIDENTIAL WATER DEMAND MODELLING." Journal of Economic Surveys 22, no. 5 (July 24, 2008): 842–71. http://dx.doi.org/10.1111/j.1467-6419.2008.00551.x.

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21

Stevens, T. H., Jonathan Miller, and Cleve Willis. "EFFECT OF PRICE STRUCTURE ON RESIDENTIAL WATER DEMAND." Journal of the American Water Resources Association 28, no. 4 (August 1992): 681–85. http://dx.doi.org/10.1111/j.1752-1688.1992.tb01489.x.

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22

Kenney, Douglas S., Christopher Goemans, Roberta Klein, Jessica Lowrey, and Kevin Reidy. "Residential Water Demand Management: Lessons from Aurora, Colorado1." JAWRA Journal of the American Water Resources Association 44, no. 1 (February 2008): 192–207. http://dx.doi.org/10.1111/j.1752-1688.2007.00147.x.

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23

Gaudin, S. "Effect of price information on residential water demand." Applied Economics 38, no. 4 (March 10, 2006): 383–93. http://dx.doi.org/10.1080/00036840500397499.

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24

Kotagama, Hemesiri, Slim Zekri, Rahma Al Harthi, and Houcine Boughanmi. "Demand function estimate for residential water in Oman." International Journal of Water Resources Development 33, no. 6 (October 6, 2016): 907–16. http://dx.doi.org/10.1080/07900627.2016.1238342.

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25

Polebitski, Austin S., and Richard N. Palmer. "Seasonal Residential Water Demand Forecasting for Census Tracts." Journal of Water Resources Planning and Management 136, no. 1 (January 2010): 27–36. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000003.

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26

Sebri, Maamar. "A meta-analysis of residential water demand studies." Environment, Development and Sustainability 16, no. 3 (September 29, 2013): 499–520. http://dx.doi.org/10.1007/s10668-013-9490-9.

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27

Custódio and Ghisi. "Assessing the Potential for Potable Water Savings in the Residential Sector of a City: A Case Study of Joinville City." Water 11, no. 10 (October 4, 2019): 2074. http://dx.doi.org/10.3390/w11102074.

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The objective of this study is to evaluate the potential for potable water savings by using rainwater in the residential sector of Joinville, a city located in southern Brazil. Data on roof areas of residential buildings were obtained from the Joinville city council. By considering the roof areas and typologies of residential buildings, representative models were created. The following parameters were used to determine the rainwater tank capacity: the number of dwellers; the total daily water demand per capita; and the rainwater demand. To carry out the simulations for determining the optimal rainwater tank sizes and potential for potable water savings, the computer program Netuno was used to run 33,720 different scenarios. By considering the occurrence percentage for each representative building model (weighted average), the average potential for potable water savings by using rainwater was calculated. The average potential in the central region of Joinville was 18.5% when there is rainwater use only in toilets, and 40.8% when there is rainwater use in toilets and washing machines. The rainwater harvesting system showed a better performance for a rainwater demand equal to 20% of the total daily water demand. The results indicate the necessity to properly size rainwater tank capacities to meet water demands, thereby encouraging more people to adopt rainwater harvesting as an alternative source for non-potable water in buildings. The demand for rainwater should be carefully evaluated, especially in multi-story residential buildings, due to the low availability of roof areas.

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28

Alcocer Yamanaka, Víctor Hugo, and Velitchko G. Tzatchkov. "Neyman-Scott-based water distribution network modelling." Ingeniería e Investigación 32, no. 3 (September 1, 2012): 32–36. http://dx.doi.org/10.15446/ing.investig.v32n3.35937.

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Residential water demand is one of the most difficult parameters to determine when modelling drinking water distribution networks. It has been proven to be a stochastic process which can be characterised as a series of rectangular pulses having set intensity, duration and frequency. Such parameters can be determined using stochastic models such as the Neyman-Scott rectangular pulse model (NSRPM). NSRPM is based on resolving a non-linear optimisation problem involving theoretical moments of the synthetic demand series (equiprobable) and of the observed moments (field measurements) statistically establishing the measured demand series. NSRPM has been applied to generating local residential demand. However, this model has not been validated for a real distribution network with residential demand aggregation, or compared to traditional methods (which is dealt with here). This paper compares the results of synthetic stochastic demand series (calculated using NSRPM applied to determining pressure and flow rate) to results obtained using traditional simulation methods using the curve of hourly variation in demand and to actual pressure and flow rate measurements. The Humaya sector of Culiacan, Sinaloa, Mexico, was used as study area.

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29

Lavee, D., Y. Danieli, G. Beniad, T. Shvartzman, and T. Ash. "Examining the effectiveness of residential water demand-side management policies in Israel." Water Policy 15, no. 4 (March 26, 2013): 585–97. http://dx.doi.org/10.2166/wp.2013.146.

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Increasing global water shortage is enhancing the need for water management policies, such as water demand policies. This study presents the main water demand-side management policies implemented in Israel, designed to reduce water demand in the urban sector, and subsequently examines their effectiveness by an econometric model, based on residential water consumption data. The main findings indicate that, among the economic policy tools, a smooth increase of water tariffs was not effective, while a drought surcharge led to a significant reduction in residential water demand. Educational policy tools also significantly reduced water demand, though the daily report on the Kinneret water level (a long-term educational tool) had a larger effect on residential water consumption than awareness campaigns (a short-term educational tool). These results may assist policymakers to make informed decisions regarding the implementation of such policy tools.

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30

García-Valiñas, María Ángeles, and Sara Suárez-Fernández. "Are Economic Tools Useful to Manage Residential Water Demand? A Review of Old Issues and Emerging Topics." Water 14, no. 16 (August 18, 2022): 2536. http://dx.doi.org/10.3390/w14162536.

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The analysis of residential water demand has long attracted attention from researchers. However, the central topics at issue have evolved considerably, transitioning from estimating price and income elasticities to using experimental techniques that assess how to motivate households towards water conservation. In this literature review, we contribute to the existing literature by giving an updated overview of the state of the art in the central topics regarding residential water demand. Moreover, we present some interesting lines of research to be explored in the future. Thus, we first review some traditional key drivers of residential water demand. Second, we discuss the role of public policies when managing residential water demand, paying special attention to pricing tools. Next, we briefly review some of the methodological issues with respect to traditional econometrics and discuss related modeling. We then discuss the role of experimental designs and nudging on residential water use. Finally, we include a summary of the main literature findings, and close the discussion introducing some emerging and promising research topics.

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31

Baerenklau, K. A., K. A. Schwabe, and A. Dinar. "The Residential Water Demand Effect of Increasing Block Rate Water Budgets." Land Economics 90, no. 4 (October 3, 2014): 683–99. http://dx.doi.org/10.3368/le.90.4.683.

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32

Hansen, Lars Garn. "Water and Energy Price Impacts on Residential Water Demand in Copenhagen." Land Economics 72, no. 1 (February 1996): 66. http://dx.doi.org/10.2307/3147158.

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33

Rauf, Tamkinat, and M. Wasif Siddiqi. "Price-setting for Residential Water: Estimation of Water Demand in Lahore." Pakistan Development Review 47, no. 4II (December 1, 2008): 893–906. http://dx.doi.org/10.30541/v47i4iipp.893-906.

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The population of Lahore has roughly doubled over the past twenty years, and an increase of two million is expected by the year 2020 [UN (2005)]. This has important implications for city planning as demand for housing, electricity, water, sanitation, public health, education, and infrastructure grows accordingly. Water and Sanitation Agency (WASA), the city’s official water supplier, has often responded to the growing demand by offering the supply-side solution: augmenting supply capacity by exploiting new water resources.1 Such investments are costly, but in view of the public good nature of water, WASA has kept tariffs well below the costrecovery level, relying on heavy loans and subsidies. While this arrangement may have worked in the past, it is now becoming increasingly unsustainable, because (1) WASA is facing severe financial constraints and which has led to poor service and underinvestment, and (2) the environmental cost of extracting water is increasing. With its low tariff rates and continually increasing costs, the WASA Lahore is unable to meet even its operation and management (O&M) costs [WASA (2007)]. WASA has been receiving financial assistance from the provincial and Lahore district governments as well as international donors in the form of grants and loans with the grant element gradually diminishing over the passage of time. In 2007, WASA currently owed Rs 5.6 billion to these agencies [WASA (2007)]. Deteriorating financial situation has also led to short-term planning, reactive operational strategy, and underinvestment in asset maintenance, future capacity, IT equipment, management and accounting information system, and training [IFC (2005)]. Consequently, WASA has shown suboptimal performance: low pressure and irregular supply, leakages, poor customer service, etc.

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34

Houk, Eric E. "Estimating residential water demand in the absence of volumetric water pricing." Global Business and Economics Review 12, no. 3 (2010): 196. http://dx.doi.org/10.1504/gber.2010.034893.

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35

Martin, Randolph C., and Ronald P. Wilder. "Residential Demand for Water and the Pricing of Municipal Water Services." Public Finance Quarterly 20, no. 1 (January 1992): 93–102. http://dx.doi.org/10.1177/109114219202000106.

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36

Zhang, B., K. H. Fang, and K. A. Baerenklau. "Have C hinese water pricing reforms reduced urban residential water demand?" Water Resources Research 53, no. 6 (June 2017): 5057–69. http://dx.doi.org/10.1002/2017wr020463.

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37

Pan, Wenxiang, Baodeng Hou, Ruixiang Yang, Xuzhu Zhan, Wenkai Tian, Baoqi Li, Weihua Xiao, et al. "Conceptual Framework and Computational Research of Hierarchical Residential Household Water Demand." Water 10, no. 6 (May 27, 2018): 696. http://dx.doi.org/10.3390/w10060696.

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Although the quantity of household water consumption does not account for a huge proportion of the total water consumption amidst socioeconomic development, there has been a steadily increasing trend due to population growth and improved urbanization standards. As such, mastering the mechanisms of household water demand, scientifically predicting trends of household water demand, and implementing reasonable control measures are key focuses of current urban water management. Based on the categorization and characteristic analysis of household water, this paper used Maslow’s Hierarchy of Needs to establish a level and grade theory of household water demand, whereby household water is classified into three levels (rigid water demand, flexible water demand, and luxury water demand) and three grades (basic water demand, reasonable water demand, and representational water demand). An in-depth analysis was then carried out on the factors that influence the computation of household water demand, whereby equations for different household water categories were established, and computations for different levels of household water were proposed. Finally, observational experiments on household water consumption were designed, and observation and simulation computations were performed on three typical households in order to verify the scientific outcome and rationality of the computation of household water demand. The research findings contribute to the enhancement and development of prediction theories on water demand, and they are of high theoretical and realistic significance in terms of scientifically predicting future household water demand and fine-tuning the management of urban water resources.

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38

Reynaud, Arnaud, and Giulia Romano. "Advances in the Economic Analysis of Residential Water Use: An Introduction." Water 10, no. 9 (August 30, 2018): 1162. http://dx.doi.org/10.3390/w10091162.

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The aim of this Special Issue is to gather evidence on the impact of price policies (PP) and non-price policies (NPP) in shaping residential water use in a context of increased water scarcity. Indeed, a large body of the empirical economic literature on residential water demand has been devoted to measuring the impact of PP (water price increases, use of block rate pricing or peak pricing, etc.). The consensus is that the residential water demand is inelastic with respect to water price, but not perfectly. Given the low water price elasticity, pricing schemes may not always be effective tools for modifying household water behaviors. This is puzzling since increasing the water price is still viewed by public authorities as the most direct economic tool for inducing water conservation behaviors. Additional evidence regarding the use of PP in shaping residential water use is then required. More recently, it has been argued that residential consumers may react to NPP, such as water conservation programs, education campaigns, or smart metering. NPP are based on the idea that residential water users can implement strategies that will result in water savings via changing their individual behaviors. Feedback information based on smart water metering is an example of approach used by some water utilities. There are still large gaps in the knowledge on the residential water demand, and in particular on the impact of PP and NPP on residential water use, household water affordability and water service performance. These topics are addressed in this Special Issue “Advances in the Economic Analysis of Residential Water Use”.

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39

Pieterse-Quirijns, E. J., E. J. M. Blokker, E. van der Blom, and J. H. G. Vreeburg. "Non-residential water demand model validated with extensive measurements." Drinking Water Engineering and Science Discussions 5, no. 1 (August 24, 2012): 455–71. http://dx.doi.org/10.5194/dwesd-5-455-2012.

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Abstract. Existing guidelines related to the water demand of non-residential buildings are outdated and do not cover hot water demand for the appropriate selection of hot water devices. Moreover, they generally overestimate peak demand values required for the design of an efficient and reliable water system. Recently, a procedure was developed based on the end-use model SIMDEUM® to derive design rules for peak demand values of both cold and hot water during various time steps for several types and sizes of non-residential buildings, i.e. offices, hotels and nursing homes. In this paper, the design rules are validated with measurements of cold and hot water patterns on a per second base. The good correlation between the simulated patterns and the measured patterns indicates that the basis of the design rules, the SIMDEUM simulated standardised buildings, is solid. Moreover, the SIMDEUM based rules give a better prediction of the measured peak values for cold water flow than the existing guidelines. Furthermore, the new design rules can predict hot water use well. In this paper it is illustrated that the new design rules lead to reliable and improved designs of building installations and water heater capacity, resulting in more hygienic and economical installations.

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40

Gutzler, David S., and Joshua S. Nims. "Interannual Variability of Water Demand and Summer Climate in Albuquerque, New Mexico." Journal of Applied Meteorology 44, no. 12 (December 1, 2005): 1777–87. http://dx.doi.org/10.1175/jam2298.1.

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Abstract The effects of interannual climate variability on water demand in Albuquerque, New Mexico, are assessed. This city provides an ideal setting for examining the effects of climate on urban water demand, because at present the municipal water supply is derived entirely from groundwater, making supply insensitive to short-term climate variability. There is little correlation between interannual variability of climate and total water demand—a result that is consistent with several previous studies. However, summertime residential demand, which composes about one-quarter of total annual demand in Albuquerque, is significantly correlated with summer-season precipitation and average daily maximum temperature. Furthermore, regressions derived from year-to-year changes in these variables are shown to isolate the climatic modulation of residential water demand effectively. Over 60% of the variance of year-to-year changes in summer residential demand is accounted for by interannual temperature and precipitation changes when using a straightforward linear regression model, with precipitation being the primary correlate. Long-term trends in water demand follow population growth closely until 1994, after which time a major water conservation effort led to absolute decreases in demand in subsequent years. The effectiveness of the conservation efforts can be quantified by applying the regression model, thus removing the year-to-year variations associated with short-term climate fluctuations estimated from the preconservation period. The preconservation regression provides a good fit to interannual summer residential demand in subsequent years, demonstrating that the regression model has successfully isolated the climatic component of water demand. The quality of this fit during a period of sharply reduced demand suggests that the conservation program has effectively targeted the nonclimatically sensitive component of water demand and has sharpened the climatically sensitive component of demand to a level closer to the consumption that is “climatically needed.”

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41

Romano, Giulia, Nicola Salvati, and Andrea Guerrini. "Estimating the Determinants of Residential Water Demand in Italy." Water 6, no. 10 (September 30, 2014): 2929–45. http://dx.doi.org/10.3390/w6102929.

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42

Arbués, Fernando, Inmaculada Villanúa, and Ramón Barberán. "Household size and residential water demand: an empirical approach." Australian Journal of Agricultural and Resource Economics 54, no. 1 (January 2010): 61–80. http://dx.doi.org/10.1111/j.1467-8489.2009.00479.x.

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43

Ghavidelfar, Saeed, Asaad Y. Shamseldin, and Bruce W. Melville. "Future implications of urban intensification on residential water demand." Journal of Environmental Planning and Management 60, no. 10 (December 6, 2016): 1809–24. http://dx.doi.org/10.1080/09640568.2016.1257976.

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44

Martin, William E., and John F. Thomas. "Policy relevance in studies of urban residential water demand." Water Resources Research 22, no. 13 (December 1986): 1735–41. http://dx.doi.org/10.1029/wr022i013p01735.

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45

Mieno, Taro, and John B. Braden. "Residential Demand for Water in the Chicago Metropolitan Area1." JAWRA Journal of the American Water Resources Association 47, no. 4 (April 11, 2011): 713–23. http://dx.doi.org/10.1111/j.1752-1688.2011.00536.x.

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46

Binet, Marie-Estelle, Fabrizio Carlevaro, and Michel Paul. "Estimation of Residential Water Demand with Imperfect Price Perception." Environmental and Resource Economics 59, no. 4 (December 10, 2013): 561–81. http://dx.doi.org/10.1007/s10640-013-9750-z.

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Ferreira, Tiago de VG, and Orestes M. Goncalves. "Stochastic simulation model of water demand in residential buildings." Building Services Engineering Research and Technology 41, no. 5 (December 17, 2019): 544–60. http://dx.doi.org/10.1177/0143624419896248.

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Abstract:

Over the years, researchers have been conducting studies to investigate the water consumption profile in buildings; these studies have contributed to the accumulation of knowledge regarding the correct sizing of hydraulic systems in buildings. In the context of the methods for the characterization of system demand or loading values, the procedures commonly employed to obtain the project flow rate were primarily proposed in the mid-20th century. These models require revision and adaptation to the current water consumption values. In recent years, certain researchers have proposed simulation models with an application focus on water distribution systems owing to the random and temporal behavior of water demand in this system type. In this study, a water-demand stochastic simulation model in residential buildings is proposed, which encompasses the behavioral modelling of users and their interaction with the system to improve the design process of water distribution systems. Therefore, geographical and population factors (quantity, distribution, and organization) were considered for the behavioral modelling of users; regarding the system modelling, aspects related to the hydraulic system were considered, such as the relation between system components, the type of sanitary appliance, and the number of available devices. Different simulations—with several different types of showers—were conducted using the proposed model. Comparing the flows obtained from the simulation and from the Brazilian standard, for all system components, the decrease in the project flow rate varied from 4% to 61%. In terms of material consumption regarding the pipe (PVC), the decrease varied from 25% to 63%. Practical application: When assessing potential designs for components in water distribution systems in buildings robust information is required for water demand across different time scales. The use of simulation models represents an important advance for the dimensioning process of these components, since it is possible to know a wider range of information about the system demand possibilities. The use of this type of model, as discussed in this article, will equip the designer with an enhanced decision making capacity.

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Rosenberg, David E. "Residential Water Demand under Alternative Rate Structures: Simulation Approach." Journal of Water Resources Planning and Management 136, no. 3 (May 2010): 395–402. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000046.

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Lee, Dongwoo, and Sybil Derrible. "Predicting Residential Water Demand with Machine-Based Statistical Learning." Journal of Water Resources Planning and Management 146, no. 1 (January 2020): 04019067. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001119.

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Bennett, Christopher, Rodney A. Stewart, and Cara D. Beal. "ANN-based residential water end-use demand forecasting model." Expert Systems with Applications 40, no. 4 (March 2013): 1014–23. http://dx.doi.org/10.1016/j.eswa.2012.08.012.

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