How high-frequency data is embedding resilience in the water sector

By Geoff McGrath, Managing Director of data science business, CKDelta

The water sector is at a crossroads. With consistent climate-related shocks such as droughts causing significant issues to our already dwindling supply and ever-increasing maintenance and operational costs for our aging infrastructure, the challenges facing the sector are seemingly reaching their peak. Regulatory drivers from the upcoming Asset Management Period (AMP8) – due to be enacted in 2025 – promise a new wave of innovation in delivering sustainability that will necessitate a best practice approach to establishing resilience in the sector. Especially as forecast household demand levels are set to exceed those required to deliver on the sectors’ net zero and efficiency targets.

Just as the challenges faced are tough, the opportunities to remediate them already exist. Innovations in the deployment of high-frequency behavioural data can help reduce cost, drive efficiency and deliver a positive ROI for water companies and their investors – all while enabling them to lead the charge in delivering net-zero carbon emissions across the value chain.

Establishing resilience through predictive analytics

Predictive analytics is a key technology in meeting the scale of this challenge and in successfully deploying the right data. As a form of modelling used to determine future outcomes, its forecasting capabilities become more accurate as more high-quality data is fed into it. Leveraging behavioural data against multiple combined data sets through predictive analytics to drive value and meet efficiency targets can enable asset managers and planners to meet these challenges more accurately.

Already we have seen considerable industry success in the deployment of these data models with the use of ‘digital twins’ at the design and planning stage. Using behavioural data, they can enable operators to create a human-in-the-loop simulator to advance early understandings of ‘what if’ moments in the management and planning of new assets and then provide decision support tools during systems operation.

Driving collaboration through open data

Utilising predictive analytics-powered data sets on open data platforms can also enable developers and operators to build more accurate forecast models that account for complex variables. Whole systems models, i.e. digital platforms that show interrelated systems data, can provide better visibility for competing KPIs (i.e. carbon reductions and systems optimisation). These platforms will be essential as these KPIs risk running into closer conflict in the push to net-zero.

Open data is already being used to great success. Visualisation maps that bring in data from multiple different sectors can enable stakeholders to better understand the impact of one team’s interventions against another’s performance. More importantly, it provides the evidence for strategic planners and decision-makers to make more informed decisions by running scenario simulations within a decision cycle. These platforms have already been utilised to great success across industries in mitigating the impact of construction on existing infrastructure, for example.

As a scalable solution, encouraging open-data sharing frameworks across the industry can help the water sector eliminate planning inefficiencies, reduce outages and deliver considerable cost benefits. All whilst driving the necessary cross-industry collaboration to unlock innovative new methods for delivering net zero.

Now is the time to act to secure our collective environmental aims and objectives. With new regulatory drivers on the horizon – including the UK water sector’s AMP8 cycle – ongoing discussions around keeping consumer costs low in the mission to reach net-zero are beginning to define the long-term strategic vision of the sector. Now, we need the right tools and platforms to deliver it. We do not have long to get this right.

Predictive analytics with machine learning capabilities has a significant role to play in enabling the sector to decarbonise. The potential to integrate this data across the value chain means we can re-conceptualise how we think about, and deploy, systems with both embedded and adaptive intelligence to optimise system performance without compromising the net-zero goal.

About the author: Geoff McGrath is an entrepreneur, strategist, innovator and technologist. For over eight years, Geoff was the former chief innovation officer for McLaren Applied, where he took insights from the world of Formula 1 to drive change in the wider transport sector and beyond, Geoff is now the Managing Director of data science business, CKDelta.

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