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<p>The optimised operation of drinking water supply infrastructures is a crucial objective for companies and cities, facing the complexity of multiple conflicting objectives and factors such as climate change and demand fluctuations. This paper presents an intelligent and predictive control system designed for the efficient and safe management of these resources. The proposed system combines a multi-agent model with Artificial Intelligence algorithms. It uses the KNN algorithm for consumption prediction, highlighting its high accuracy (average relative error of 6%) and its ability to provide interpretable results that reflect human expert knowledge, avoiding the “black box” behaviour of other AIs. An essential component is the simulation tool developed. This allows the configuration, testing, validation and adjustment of the control system and its hyperparameters in a safe environment, preventing damage to critical infrastructure and optimising investments. The solution has been successfully validated in two real drinking water infrastructures in south-eastern Spain (serving 5,000 and 90,000-200,000 inhabitants). The results demonstrate a tangible improvement in pressure stabilisation, maintenance of optimal water levels (reducing unnecessary storage and the formation of by-products) and minimisation of pump starts/stops, which translates into energy savings and a longer service life for the equipment. In addition, this approach is adaptable to various infrastructure configurations.</p>

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From Planning to Control: an Intelligent System for the Optimisation of Drinking Water Resources

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月16日

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  • Carlos Calatayud Asensi,
  • Jose Vicente Berná Martínez,
  • Lucía Arnau Muñoz,
  • David Saavedra Pastor

摘要

The optimised operation of drinking water supply infrastructures is a crucial objective for companies and cities, facing the complexity of multiple conflicting objectives and factors such as climate change and demand fluctuations. This paper presents an intelligent and predictive control system designed for the efficient and safe management of these resources. The proposed system combines a multi-agent model with Artificial Intelligence algorithms. It uses the KNN algorithm for consumption prediction, highlighting its high accuracy (average relative error of 6%) and its ability to provide interpretable results that reflect human expert knowledge, avoiding the “black box” behaviour of other AIs. An essential component is the simulation tool developed. This allows the configuration, testing, validation and adjustment of the control system and its hyperparameters in a safe environment, preventing damage to critical infrastructure and optimising investments. The solution has been successfully validated in two real drinking water infrastructures in south-eastern Spain (serving 5,000 and 90,000-200,000 inhabitants). The results demonstrate a tangible improvement in pressure stabilisation, maintenance of optimal water levels (reducing unnecessary storage and the formation of by-products) and minimisation of pump starts/stops, which translates into energy savings and a longer service life for the equipment. In addition, this approach is adaptable to various infrastructure configurations.