REAL-TIME PATTERN-BASED WATER DEMAND FORECASTING FOR SMART WATER NETWORKS
A revolutionary short-term demand forecasting software, DemandWatch changes the way water utilities around the world leverage historical demand data to estimate future water demands for real-time, near-optimal control and management of their water distribution systems. Its primary strength lies in its adaptive demand forecasting process, which can water utilities can better plan, operate and manage their water distribution systems, improve conservation measures, minimize energy consumption, meet regulatory compliance, and enhance customer service.
SOPHISTICATED WATER DEMAND FORECASTING
analyzes patterns from historical demand data and uses a other pertinent reporting tools. Its adaptive learning process enables it to continuously generate predictions, taking observations from the recent past and predicting demand for the near future. Even if there is a loss of observed telemetry data, DemandWatch can still generate predictions from cyclical components of the model.
POWERFUL REPORTING TOOLS
used to identify seasonal and weekly periodicities in daily water demands as well as daily periodicities in hourly water generated to reveal patterns between days and hourly daily and hourly cyclical patterns. Autoregressive terms add predictions, greatly increasing the accuracy of forecasted values. DemandWatch generates water demand forecasts IWLive Pro can readily use the output of DemandWatch to perform accurate simulations that reliably predict network operational performance over the next few hours or days. The results of these simulations can then be used for optimal operation and management of water distribution systems.
HANDLING ABRUPT CHANGES IN WEATHER CONDITION (TEMPERATURE CHANGES)
DemandWatch takes into account unexpected variations in temperature, which is especially important during unusually hot summer days. It models the effect of abrupt temperature changes on daily demand by taking the difference between the temperature forecast and the historical average temperature for that particular season. This difference in temperature is then multiplied by a regression factor and added to the predicted daily demand. If the weather forecast is hotter than average, the predicted demand will be increased. If the weather forecast is not available, DemandWatch will use the average weather value for that day to improve the standard demand forecast. As a result, DemandWatch will always ensure accurate demand forecasting using all available data.
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