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<p>Effective management of a water supply system amid urban infrastructure growth requires considering numerous factors that determine its technological and energy operational modes. Energy consumption in water intake structures can be optimized through adaptive activation of pumping stations, factoring in equipment timing and composition. A key aspect of implementing such measures is accurately forecasting water consumption dynamics. Most studies analyze the consequences of water consumption factors but overlook their potential for rapid acquisition in building predictive models or their systematization for use in water supply management systems. In contrast, this study aims to identify statistically significant factors that can be rapidly obtained and applied in machine learning models to forecast water consumption. It uses data on the water supply regime of Gomel, a major industrial center in Belarus with over 500,000 residents, from 2017 to 2023. Factors considered include air temperature, precipitation, and temporal parameters, such as hours of the day and months of the year, examining their influence on water consumption dynamics and energy costs within the system. The methodological framework employs correlation and regression analyses to assess relationships among factors, using smoothing (moving average), filtering, and truncation by temperature thresholds, ANOVA, and Tukey’s test to process data and evaluate differences among groups. Results indicate that temperatures above 25 °C increase water consumption by 15.8% and energy costs by 15.6%. In cold periods, temperature effects are minimal, but temporal factors significantly influence consumption patterns. Density analysis identified two stable clusters of hourly water consumption.</p>

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Identification of Easily Accessible Urban Water Consumption Factors for Energy-Efficient Management of Pumping Stations

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Aliaksei Kapanski,
  • Nadzeya V. Hruntovich,
  • Roman V. Klyuev,
  • Vladimir Brigida

摘要

Effective management of a water supply system amid urban infrastructure growth requires considering numerous factors that determine its technological and energy operational modes. Energy consumption in water intake structures can be optimized through adaptive activation of pumping stations, factoring in equipment timing and composition. A key aspect of implementing such measures is accurately forecasting water consumption dynamics. Most studies analyze the consequences of water consumption factors but overlook their potential for rapid acquisition in building predictive models or their systematization for use in water supply management systems. In contrast, this study aims to identify statistically significant factors that can be rapidly obtained and applied in machine learning models to forecast water consumption. It uses data on the water supply regime of Gomel, a major industrial center in Belarus with over 500,000 residents, from 2017 to 2023. Factors considered include air temperature, precipitation, and temporal parameters, such as hours of the day and months of the year, examining their influence on water consumption dynamics and energy costs within the system. The methodological framework employs correlation and regression analyses to assess relationships among factors, using smoothing (moving average), filtering, and truncation by temperature thresholds, ANOVA, and Tukey’s test to process data and evaluate differences among groups. Results indicate that temperatures above 25 °C increase water consumption by 15.8% and energy costs by 15.6%. In cold periods, temperature effects are minimal, but temporal factors significantly influence consumption patterns. Density analysis identified two stable clusters of hourly water consumption.