Novel bottom-up urban water demand forecasting model: Revealing the determinants, drivers and predictors of residential indoor end-use consumption

Makki, Anas A.; Stewart, Rodney Anthony; Beal, Cara; Panuwatwanich, Kriengsak

doi:10.1016/j.resconrec.2014.11.009

Cited by 84 publications

(59 citation statements)

References 61 publications

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“…To name a few, from the consumer's perspective, water conservation measures could be tailored to each individual consumer [6], thus maximizing the saving potential in each case, or the variable term in the tariff could be designed according to consumer's characteristics to guarantee the balance between equity and income [7]. Furthermore, from the utility's view, water demand prediction models could be reliably produced from a more accurate bottom-up approach [8,9].…”

Section: Introductionmentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis.

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Section: Introductionmentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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“…Several studies agreed on the insignificant effect of educational level on total water consumption [16,48]. When segregating water consumption for each end-use, education level is a significant determinant for shower/bath and dishwasher end-use categories [16,58]. Willis et al (2010) advocated the use of smart meter coupled with data logger for end-use analysis and real-time feedback provision [20].…”

Section: Discussion and Possible Topics For Further Researchesmentioning

confidence: 99%

A Review of Residential Water Consumption Determinants

Bich-Ngoc

Teller

2018

Computational Science and Its Applications – ICCSA 2018

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Abstract. Water supply sectors are facing higher uncertainty in both resource availability and consumer demand. Future conservation programs require a full understanding of underlying factors of residential water consumption. However, previous studies have only considered one or several groups of factors without putting them all together in a bigger picture. This study was developed to provide a comprehensive view on these determinants and their relationships, as well as to discuss current gaps and possible directions. Determinants are categorized into six groups: (1) Economic; (2) Socio-demographic; (3) Physical properties; (4) Technological; (5) Climatic; and (6) Spatial drivers. All these determinants produce a very complex picture with many possible interrelationships. This nature, in one hand, poses challenges in selecting suitable technique to avoid autocorrelation, but on the other hand, provides chances to substitute unavailable important data with proxy variables. We have emphasized the lack of regional and cultural diversity in current studies, as most of them were carried out in developed and arid areas. Hence, a wider range of country specific and local-based studies is needed to better reflect the determinants and their relationships in diverse contexts. In future studies, a broader assessment scope taking into account effects such as feedback loop, spillover, and rebound should also be considered. In addition, these studies must deal with modern issues such as balancing between smart monitoring device utilization and consumer privacy.

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“…SmartH2O the gamified incentive model has proven to be capable of eliciting user-generated data about the household composition, water consuming appliances, presence of a garden or balcony and other factors that determine water consumption levels. These data allow utilities to model the water demand beyond what can be inferred from aggregated (smart metered) readings by clustering consumers based on socio-psychographic features and/or disaggregating consumption to the level of water consumption appliances [73], [74].  Experiences from SmartH2O and other projects [15] [75] strongly suggest that the sustainability of effects on water consumption behavior cannot be taken for granted.…”

Section: Evaluating User Responses and First Effects On Water Consumpmentioning

confidence: 99%