The water sector is at a crossroads. With the threat posed by climate change to fresh water availability 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 – promises a new wave of innovation in delivering sustainability that will necessitate a best practice approach to establishing resilience in the sector.
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 are 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 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.
Being able to forecast new patterns of consumption against variables such as predicting future scarcity of supply and the need to drive sector efficiency can keep resilience firmly embedded in our strategic thinking for the sector.
Driving collaboration through open data
Utilising predictive analytics-powered data sets on open data platforms can enable developers and operators to build more accurate forecast models that accounts for complex variables. Whole systems models, i.e. digital platforms that shows 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 planners to reduce the impact on existing underground infrastructure during construction. Encouraging open-data sharing frameworks across 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 cost 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.
You can read more in CKDelta’s latest report: Pioneering cross-sector change and collaboration: How high-frequency data is driving the transition to net zero.
This article was originally published in Smart Water Magazine
Feature Image Source: Arek Socha from Pixabay