project

Peak consumption causes

Water companies need a water consumption model to help them size their production locations. In this project KWR will extend the day factor model (Vonk et al., 2017) with area-specific information, including garden acreage, and number of residents and swimming pools.

By explicitly entering this information into the model, its application potential is extended; it will make clear which aspects are determinative of water consumption and thus also directly predict the day production, instead of only the day factor.

Towards area-specific model for day production

Water companies need a water consumption model to help them size their production locations. A model has already been developed by Vonk et al. (2017), which predicts day factors (day production, standardised to annual average day production) on long time scales. This model is fed with daily measurements from the KNMI and vacation-absence statistics from the CBS, and produces predictions for the day factors and peak factors under various (climate) scenarios. However, since the model is calibrated separately for each supply area, the relations, for instance with garden acreage, number of residents, number of swimming pools, and rainwater collection, are implicit. WML and Brabant Water have expressed their desire to have the model extended, with the aim of gaining better insight into the underlying factors that determine water demand on a given day.

Model extension for more insight

In this project KWR will extend the existing ‘day factor model’ with area-specific information, including garden acreage, and number of residents and swimming pools. By explicitly entering this information into the model, one can gain more insight into which aspects are determinative of water consumption. Moreover, it will make it possible to directly predict the day production, instead of only the day factor.

A comprehensible model with real-time data links

The research project’s objective was to develop a model for Brabant Water and WML which, on the basis of measurement series of day production, time series from the KNMI and area-specific characteristics, can describe and predict variations in day production. In addition, a link with operational data systems is made, so that the predictions can be easily updated on the basis of the most recent measurements.