How SCADA Data Drives Smarter Water Loss Prioritization
Water loss is a massive global issue costing an estimated $39 billion USD per year (Liemberger and Wyatt 2019).
Excessive leakage also forces utilities to consider billions of dollars in investments in new water sources, such as desalination plants.
Most utilities rely on spreadsheets to prioritize water loss maintenance.
But with a 1-million-row limit, spreadsheets barely scratch the surface when analyzing SCADA data for evidence of leakage.
By adopting a modern big data tool, utilities can unlock their SCADA data, gaining actionable insights to reduce water loss and safeguard water supplies.
How Does SensorClean Help?
SensorClean makes it easy to conduct a water balance on subsystems i.e. District Metered Areas (DMAs).
It then ranks DMAs for maintenance prioritization based on water loss metrics.
More specifically, SensorClean:
quantifies DMA non-revenue water with data gap-filling technology and customer data
estimates leakage from minimum night flow (MNF) baselines
quantifies pipe failures and hydrant water theft; and
identifies sensor issues for proactive maintenance.
Ranking DMAs for non-revenue water prioritization
DMA non-revenue water (NRW) is the difference between the DMA’s resultant water balance, and the volume of water that was paid for by customers, over the same period (IWA 2000).
While utilities measure NRW for their entire system (e.g., from treatment plants or dam outflows), analyzing losses at the DMA level is complex with spreadsheets – and therefore often gets overlooked.
A key issue is sensor dropouts in the SCADA data – which occur for a range of reasons.
SensorClean fills data gaps distinguishing between weekday and weekend behaviors, amongst other things.
Setting night flow baselines in DMAs
The minimum night flow (MNF) provides another key leakage indicator.
Seasonal trends in MNFs can help set baselines, for instance, the lowest daily MNF values typically occur in winter.
Higher values in summer may reflect night time garden irrigation.
Understanding the seasonal component of night flows allows for best estimates of DMA leakage for prioritization.
Improving cultures around data quality
Sensor downtime is a major obstacle, often overlooked because its impact on water loss is not well illustrated.
A strong data quality culture starts with understanding the ‘why.’
Data champions – those responsible for keeping sensors online – must know how their work impacts water loss reductions.
Without real-world applications, keeping sensors online feels like a checklist task rather than a strategic priority.
Smarter, data-driven water management
Quantifying DMA water loss empowers utility decision-makers to:
1. Prioritize DMAs cost effectively
2. Reduce water loss and save millions of dollars; and
3. Ensure a sustainable, resilient water supply.
Please contact us to improve water loss economics at your utility.
Liemberger, R. and A. Wyatt (2019). "Quantifying the global non-revenue water problem." Water Supply 19(3): 831-837.
International Water Association (2000). IWA Water balance and water loss performance indicators. International Water Association www.leakssuitelibrary.com/iwa-water-balance