By leveraging AI tools, utilities can enhance efficiency, optimise resource allocation, and adopt proactive maintenance strategies.
AI and machine learning (ML) is invaluable in demand forecasting, accurately predicting operational needs based on historical data, weather conditions, population growth, and other relevant factors.
This enables utilities to optimise production, distribution, and capacity management, even in the face of increasing droughts, population growth, and ageing infrastructure.
For clean water, ensuring ample water is available at the right time and place, is a clear goal, considering increasing droughts, population growth and ageing assets.
For wastewater, it covers some gaps from long-term underinvestment and focus. The drivers are similar, in that climate change and increasing populations impact networks not designed to withstand these challenges.
Predict and prevent has been Metasphere’s focus for years.
A specialist in wastewater management, Metasphere provides smart network management solutions to the global utility industry.
Its intelligent solutions offer full visibility and forecasting for remote assets and systems, reducing telemetry ownership costs.
Highly accurate hyperlocal weather forecasts are changing how councils control localised flooding, utilities manage remote raw water sources and how pollution events are reduced.
Metasphere’s mantra is ‘no spills’. The company believes shifting from a reactive monitor and alarm to proactive predict and prevent operational mentality, is the answer to achieving no spills, with technology playing a critical role.
AI/ML technology has already proven its worth in energy consumption optimisation for water utilities.
Metasphere say that by analysing energy consumption and system performance data, utilities can identify opportunities for improvement.
ML algorithms can optimise pumping schedules, adjust treatment processes based on real-time conditions, and predict energy demand. This allows utilities to operate at optimal efficiency, reducing costs and minimising environmental impact.
A significant challenge for utilities is transitioning from the management of isolated assets to automated and integrated networks. Taking a holistic approach by considering catchment areas, weather data, and utility network information allows for better prediction of impacts on natural watercourses and coastlines.
Predicting water quality, including soil moisture data and information on agricultural use, such as fertiliser by crop type and proximity to watercourses, is an obvious and valuable albeit not simple addition to catchment-based management.
Integrating data from various sources, such as traditional methods like IoT and citizen science data, is essential but presents challenges.
Overcoming these challenges is critical for utilities to fully leverage the potential of AI/ML.
Metasphere believe integrating data sources and tapping into the expertise of subject matter experts, utilities can continue improving their assets and networks.
In conclusion, AI/ML plays a critical role in water utility operations. By harnessing AI technologies, utilities can enhance efficiency, reduce costs, optimise networks, and protect the environment while delivering sustainable water services to the communities they serve.
For more information, visit metasphere.co.uk
