Beginning the journey to smart water companies starting with water networks

Transcription

1 Beginning the journey to smart water companies starting with water networks Paul Rutter, Water Innovation Manager, Thames Water Joby Boxall, Professor of Water Infrastructure Engineering, The University of Sheffield

2 Overview Why the future is data scientists How real time big data analytics are transformational and available Tentative steps : smart water networks (SMARTWATER4EUROPE) - and where next

3 Water Industry Future is Data Scientists not Engineers Degree Educated, Chartered Status Applied thinking based on specific training and education Applying technology/innovation to Asset Challenges in the framework of large capital programmes Degree Educated typically Mathematics, Stats and modellers Able to solicit insights to data sources where IT and maths join forces in presence of uncertainty Machine learning based algorithms that control complex systems and maximise existing asset efficiency

4 A future Water Control Centre

5 A future Water Control Centre Embed video here

6 A Smart Water Meter? We don t yet think BIG DATA in water industry Billing, Mass Balances for leak detection 15mins data from yesterday not real time (it s the batteries you see!) Data driven classification customer clusters Real-time Smart Water Meter Water meter also pressure sensor Add actuated network dynamic control Smart metering data (Volume) Customer & water efficiency (Variety) Leakage and levels of service (Velocity)

7 Analytics Past Future Post-mortem What has happened? (Why has it happened?) What will happen? When will it happen? Why will it happen? How do we benefit? How will these decisions impact everything else? Descriptive analytics Predictive analytics Prescriptive analytics

8 Hype cycle for Emerging Technologies, 2014

9 Previous and current machine learning work EDS Strategic Risk ANN/ FIS for leak detection SVR and BED for hydraulic anomalies CANARY WQ EDS (USEPA) CSO Analytics (ANN/ FIS) SOM and FIS stagnation pipe risk model Ensemble DT predictive model for DMA iron risk Semblance analysis EPR and SOMs for regen rates

10 SmartWater4Europe A four-year FP7 demonstration project ( ) funded by the EU Four sites allowing demonstration of solutions incorporating sensors, data processing, modelling and ICT technologies. The UK site is focused on leak management, integrating flow and pressure instrumentation, advanced (smart) metering infrastructure, novel instruments (capable of high resolution monitoring) and data analytics

11 Thames Water Demo Site 870 km of distribution mains, 172 km of trunk mains 45 Ml/d of chlorinated potable water Ø from 4 (100 mm) up to 32 (800 mm) Many pipes >60 years old 89,000 customers, 4 heavily instrumented DMAs inc. ~1000 AMI Leakage Management Find leaks soon after they occur / failure mechanisms before they occur 4 DMAs featuring absolute water balance Installation of a range of technologies Implementing / adapting data analytics

12 SW4E Demo Site activities Integration of multiple data sources: DMA meters, Burstminders, Trunkminders, Incertameters and AMR data Online sensor data storage and availability Implement work flows and services on Cloud based portal called youshare AURA-Alert online enhancements for WDS specific processing and settings including sequence of states event detection Optimal instrumentation studies, integrating flow and pressure Validation trials and cost benefit analysis

13 Data Adaptor Store Search Input pattern Analytics: AURA-Alert Advanced Uncertain Reasoning Architecture (AURA) Alert utilises the power of associative memories for novelty detection: Provides means to train system on normality without complex underlying models Validate unknown data against stored states to see if unusual Highly scalable approach particularly for high volumes of Big Data Benefits from one-shot and high speed training Yet to be applied for water distribution systems Candidate Selector binary Store AURA SearchEngine Output pattern Indexer Candidate Engine (Back check) Result Correlation Matrix Memories Indexes or Data Results

14 Example Zoom of a detected event that was correlated with customer contact and leak repair information. A Condition Monitoring approach for networks can allow the early detection of potential faults in assets Combinations of instruments will be used to provide fine grained alerts

15 Future Networks Data collection strategies for Big Data (macro v micro) collecting the right data at the right time from the right place to inform on network performance Knowledge discovery and derived intelligence from new predictive / prescriptive analytics Including local deployment A condition monitoring approach for that allow early detection of potential faults in assets Cloud, grid and SaaS Smart Wastewater Networks

16 Automated CSO level and rainfall analysis

17 Analytics progressing fast to realise a very different future Are organisations and people adapting and embracing change?

18 Further details: Mounce, S. R., Mounce, R. B., Jackson, T., Austin, J. and Boxall, J. B. (2014). Associative neural networks for pattern matching and novelty detection in water distribution system time series data. Journal of HydroInformatics. Vol. 16 (3), pp With thanks to:

19 I have a million Big Data ideas, but, they all point to certain death Data to information: the Big Data issue - why is less than 0.5% of data ever analysed and used? Information is the oil of the 21 st century, and analytics is the combustion engine Big Data is not about the data it is about the analytics You can have data without information, but you cannot have information without data