Prognozowanie zapotrzebowania na wodę z wykorzystaniem uczenia maszynowego

Stańczyk, Justyna; Kajewska-Szkudlarek, Joanna; Łomotowski, Janusz; Lipiński, P.; Rychlikowski, Paweł; Konieczny, T.

doi:10.15199/17.2018.10.5

Cited by 2 publications

(2 citation statements)

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“…For instance, it is possible to classify geolocated data into trips and stops and to infer a purpose of a particular trip (Siła-Nowicka et al 2016) and then link it to an individual water usage profile, which may result in a better performance of forecasting models. Incorporating other datasets such as weather conditions or land use data could further improve model accuracy (Stańczyk et al 2018;Ghiassi, Zimbra, and Saidane 2008;Al-Zahrani and Abo-Monasar 2015).…”

Section: Geolocated Data Applicationmentioning

confidence: 99%

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Smolak

Kasieczka

Fiałkiewicz

et al. 2020

Urban Water Journal

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Section: Geolocated Data Applicationmentioning

confidence: 99%

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Smolak

Kasieczka

Fiałkiewicz

et al. 2020

Urban Water Journal

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“…Few researchers have addressed the problem of water consumption analysis in terms of water meters installed in residences. Most research has focused on the interpretation of water demand using time series registered in whole district metered areas (Fiorillo et al 2021;Stańczyk et al 2018). Therefore, the purpose of this study is to create a new deterministic method to tackle the problem associated with a lack of short-term detailed data with fuzzy time series using a multiplicative model for water consumption.…”

Section: Introductionmentioning

confidence: 99%

Water Consumption Variability Based on Cumulative Data From Non-simultaneous and Long-term Measurements

Wawrzosek

Ignaciuk

Stańczyk

et al. 2021

Water Resour Manage

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Devices for water consumption measurement provide data from periodical readings in a non-simultaneous and cumulative manner. This may result in inaccuracies within the process of inference about the short-term habitual patterns of water supply network users. Maintaining systems at the interface between periodic and continuous processes requires the continuous improvement of research methodology. To obtain reliable results regarding the variability of water consumption, the first step should be to estimate it for each observation day by periodic averaging and a possible water balancing approach, but the analysis of the value of estimators obtained in this way usually does not allow for studying autocorrelation. However, other methods indicate the existence of multiplicative parameters characterizing short- and long-term variations in water demand. The purpose of this study is to create a new and deterministic method for tackling the problem associated with a lack of short-term detailed data with fuzzy time series using a multiplicative model for water consumption. Satisfactory results have been obtained, demonstrating that the dispersed data, received in a cumulative manner for random periods of measurement, can be analyzed by the methodology of proposed statistical inference. The observed variability in water consumption may be used in the planning and modernization of water supply systems, development of water demand patterns, hydraulic models, and in the creation of forecasting models of water consumption.

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Prognozowanie zapotrzebowania na wodę z wykorzystaniem uczenia maszynowego

Cited by 2 publications

References 0 publications

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Water Consumption Variability Based on Cumulative Data From Non-simultaneous and Long-term Measurements

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