Time Series Demand model

Owned by teamcity (Deactivated)
24 Oct 2016
3 min read

A time-series demand model allows you to extract water directly from a storage and identify the amount of water required to satisfy a demand at each time-step. The model does not have to be directly downstream of the storage, the supply point will order water from the storage to meet demands.

If a confluence node is connected between a storage node and a water user demand node, the flow pathway link in the confluence must be set to Regulated for the storage to order water to satisfy the demand downstream. If it is left unregulated, water will not be ordered from the storage and the supply point will pull water from the river at the point in the network that it sits to meet the demand. if the demand is higher than the volume in the reach, then the supply point will extract everything.

If there is no storage in the network above the supply point and water user node, then the volume is extracted from the river at the point where the supply point sits in the network.

Figure 1 shows the feature editor for the Time series demand model.

Figure 1. Water user node (Time series demand model)

Time-series demands can be specified using either a single value, a time-series data file (Table 1 shows the format of a file) or an expression using the Function Editor.

Note: A time series demand model is used to ensure that there is no discrepancy in orders between leap and non-leap years when specifying a monthly pattern demand model. Use a pattern variable with daily units (rather than monthly) to allow Source to recognise the number of days in February. As an example, consider the data input for a water user demand model which has been assigned a function ($Function1), which in turn is a variable of type time series ($ts). Figure 2 shows the time series variable (notice that Results Units are specified in megalitres per day) and Figure 3 depicts the input function assigned to the demand model.
Figure 2. Time series demand model, Time series variable

Figure 3. Time series demand model, Input function

 

Refer to Return flows for details on configuring this parameter.

Table 1. Water User node (Time Series Demand, data file format)

Row

Column (comma-separated)

1

2

1

date

value

Where:

date is the date of observation in dd/mm/yyyy format (eg. 31-12-2000)

value is the observed value (eg. 2600).