Increase the efficiency of water resources management in urban water networks
Reduce waste of energy and water
Demand forecast driven optimization
Online learning and optimization (reinforcement learning)
Leakage localization:
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Simulation of several leakage scenarios for the computation of induced flow and pressure variations
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Machine Learning for inverting the relation: inferring the set of (simulated) scenarios associated to the actual flow and pressure data (from sensors)
Demand forecasting:
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Time series clustering for the identification of typical patterns
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Learning a forecasting model for each identified pattern
Pump scheduling optimization:
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Global Optimization using hydraulic simulation and demand forecasts
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Reinforcement Learning for online control/optimization
Accurate (water) demand forecast(MAPE lower than 2-3%) and anomaly detection (on smart metering data)
Leakage localization (60-80% of correct localization)
Pump scheduling optimization (5-10% energy costs reduction)
