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dc.contributor.authorZadachyn V. M.-
dc.contributor.authorFrolov O. V.-
dc.date.accessioned2023-05-26T12:54:53Z-
dc.date.available2023-05-26T12:54:53Z-
dc.date.issued2023-
dc.identifier.citationZadachyn V. M. Urban daily water demand forecasting / V. M. Zadachyn, O. V. Frolov // // Вісник Харківського національного автомобільно-дорожнього університету. Збірник наукових праць. - Xарків: ХНАДУ, 2023. - Вип. 100. - С. 30-36.ru_RU
dc.identifier.urihttp://repository.hneu.edu.ua/handle/123456789/29512-
dc.description.abstractThe problem of building models and methods of forecasting the daily need for urban water is considered. Much more attention needs to be given to forecasting methods if utilities are to make decisions that reflect the level of uncertainty precisely in future daily demand forecasts. Daily water consumption, unlike annual and monthly water consumption, is much more highly dependent on chance. Goal. The main goal of this paper is to obtain enough accurate forecasts of daily urban water consumption. Method. An algorithm for calculating the urban daily water demand forecast based on the concept of same-type days of water demand for previous years has been suggested. Scientific novelty. The originality of the method lies in the fact that it does not use neural network models, but still makes it possible to obtain enough accurate forecasts of daily urban water needs. Results. The presented algorithm for calculating the urban daily water demand forecast has been implemented in the form of a software package and has been tested for many years in real-life conditions. The average absolute percentage error of the daily forecast of urban water demand for one month does not exceed 5 %. Practical significance. The practical value of this work lies in the fact that the presented software complex for calculating the forecast of the city's daily water demand can be used in the information services of city utility companies to make operational and tactical decisions regarding the provision of water supply services to the population.ru_RU
dc.language.isoenru_RU
dc.subjectforecastingru_RU
dc.subjectforecastru_RU
dc.subjecttime seriesru_RU
dc.subjectmodelru_RU
dc.subjectartificial neural networkru_RU
dc.subjectwater demandru_RU
dc.subjectwater supplyru_RU
dc.subjectstatistical analysisru_RU
dc.titleUrban daily water demand forecastingru_RU
dc.typeArticleru_RU
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