Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy
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In the present paper, a novel method is provided to detect significant daily consumption patterns and to obtain scaling laws to predict consumption patterns for groups of homogeneous users. The first issue relies on the use of Self-Organizing Map to gain insights about the initial assumption of distinct homogeneous consumption groups and to find additional clusters based on calendar dates. Non-dimensional pattern detection is performed on both residential and non-residential connections, with data provided by one-year measurements of a large-size smart water network placed in Naples (Italy). The second issue relies on the use of the variance function to explain the dependence of aggregated variance on the mean and on the number of aggregated users. Equations and related parameters’ values are provided to predict mean dimensional daily patterns and variation bands describing water consumption of a generic set of aggregated users.
KeywordsPattern detection Scaling laws Self-organizing map Variance function Water demand patterns
The Authors would like to thank ABC Acqua Bene Comune – Napoli, who installed the telemetry system and provided for consumption data.
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Conflict of interests
There is no conflict of interest.
- Adamowski J, Fung Chan H, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48(1):W01528CrossRefGoogle Scholar
- Kottegoda NT, Rosso R (2008) Applied statistics for civil and environmental engineers, 2nd edn. Wiley, UKGoogle Scholar
- Magini R, Capannolo F, Ridolfi E, Guercio R (2017) Demand uncertainty in modelling WDS: scaling laws and scenario generation. WIT Trans Ecol Environ 210:735–746Google Scholar
- McCullagh P, Nelder J (1989) Generalized linear models. Chapman & hall/CRC monographs on statistics and applied probability. CRC Press, Boca RatonGoogle Scholar
- Tricarico C, De Marinis G, Gargano R, Leopardi A (2007) Peak residential water demand. In: Proceedings of the institution of civil engineers–water management, vol 160. Thomas Telford Ltd, pp 115–121Google Scholar