From meter readings to operational decisions: AI for smart water management

The residential and flexible accommodation (flex living) sector is evolving at high speed. Tenant turnover, changing usage profiles, centralised management and maintenance of spaces, and variable occupancy pose new challenges for traditional water management systems, which are not always prepared to respond with agility and precision.

In this context, managers face mounting pressure: keeping operating costs under control, meeting sustainability targets and ensuring a quality experience for residents. Yet without detailed, contextualised information, small leaks can go unnoticed, financial discrepancies can emerge from metering errors, and decisions can rely more on intuition than on objective data.

The hIhAigua project was born with a pragmatic, scalable approach. Driven by Simon with the collaboration of Cetaqua and i2CAT, it proposes to leverage existing infrastructure — such as conventional cold water, hot water, energy and climate meters — and integrate it with an outdoor temperature probe and meteorological data to generate useful knowledge for this purpose.

All this information is consolidated in Simon's LOLA operational platform, which turns the data into actionable value for both managers and residents. Specifically, the solution integrates:

• Cold and hot water consumption data (volumes and flows).

• Energy data (kWh or power) to correlate consumption and comfort.

• Data from the building's aerothermal climate system.

• Environmental information from the surroundings, drawn from probes and external meteorological sources.

One of hIhAigua's distinguishing features is the use of explainable artificial intelligence (XAI), through which residents and managers can understand the reliability of the data shown and the reasoning behind the system's decisions. This is essential to move from anomaly detection to clear operational decisions.

The solution can identify leaks and submetering errors from patterns such as anomalous overnight consumption, sustained flat sections, cumulative jumps or unusual persistence in consumption. Each alert comes with a visual explanation that shows which factors influenced its detection — such as flow rate, duration, outside temperature or thermal variation — and with what level of confidence.

The system also includes temporal attribution, pointing to the exact intervals when the anomaly occurred and indicating what should change so the alert does not trigger again. This chain of detection, explanation and action is what turns AI into a truly operational tool.

For managers of buildings and residential assets, hIhAigua brings a clear, actionable view of operations. The dashboard offers key indicators such as water volume and the financial savings generated per period, as well as trends by building.

The solution also makes it easier to provide the traceability needed for sustainability certifications. Specifically, hIhAigua helps obtain water credits under certifications such as BREEAM, thanks to detailed submetering, seven-minute logging intervals and the ability to export data via API for audits.

The result is better maintenance and management of the building, a reduction in losses caused by incidents and a solid data foundation to strengthen ESG strategies.

hIhAigua doesn't only optimise management, it also empowers the resident. Through an intuitive web application, each user can access a daily summary of their consumption, with identified uses — such as showers or washing machines — and simple recommendations to lower their bill.

The platform also provides a view of the monthly water footprint, allowing one's own consumption to be set against that of the community, the building or the city. In this way, greater awareness is fostered without creating friction in the user experience.

hIhAigua's success rests on collaboration between different stakeholders with complementary expertise. Cetaqua brings its experience in the water field and anomaly detection; i2CAT leads usage modelling and AI explainability with criteria of transferable research and sound data governance; and Simon integrates the solution into the LOLA platform, ensuring its operational deployment at scale and with differentiated interfaces for managers and residents.

This approach reduces the typical risks of pilot projects and traces a clear path towards the real exploitation of the technology.

The hIhAigua initiative is supported by the European Union – NextGenerationEU, within the framework of the Recovery, Transformation and Resilience Plan, through the Departament d'Empresa i Treball of the Generalitat de Catalunya.