inflowSeries#
Stochastic inflow data.
Input connections |
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Output connections |
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License |
PRODRISK_OPEN |
Release version |
9.6.1 |
Attributes#
| Object type | Attribute name | Python data type | Core data type | Time series completion | unit | Default value | I/O | License | Version added | Description |
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name#
Inflow series name (unit: none)
standardDeviation#
Standard deviation in statistical model. The standard deviation in the scaled version of the inflow input (see relativeAverageInflow). (unit: none, time series completion: step curve)
outcomeValue#
Outcomes for principal components in statistical model. (unit: none)
outcomeProbability#
Probabilities for principal components in statistical model. (unit: none)
inflowScenarios#
Inflow scenarios. The number of scenarios must be the same as the number of scenarios in the price. Input should be given for at least 52 weeks (this input is used in scaling local inflow for each module connected to the inflow series). (unit: m3/s, time series completion: step curve)
histAverageInflow#
If set, this value is used in scaling of local inflow to each module. As default, the average over all scenarios for the last 52 weeks of the inflow scenarios is used. (unit: Mm3/year)
autoCorrelation#
Autocorrelation coefficients in statistical model, represents the autocorrelation of the inflowSeries with all the other inflowSeries, including itself. (unit: none)
relativeAverageInflow#
Relative average inflow in the statistical model. The relativeAverageInflow is a scaled version of the inflow input with weekly resolution. (unit: none, time series completion: step curve)
standardized_weekly_inflows#
Standarized weekly inflows, used to generate the inflow model. (unit: Mm3/week, time series completion: step curve)
seriesId#
Id used to connect module and inflowSeries (unit: none)
lognormal_centers#
Center points from k-means clustering used in the lognormal inflow model (unit: none, time series completion: step curve)
cutCoeffs#
Cut coefficients for inflow series. The inflow used in Prodrisk is normalized (normalized inflow = [inflow - mean] / stdev). The cut coefficients of the inflow is given as an array of XY tables. Each table in the array corresponds to a price level in ascending order. The x values are all 0, while the y values represent the coefficients. (unit: kEUR/normalized inflow)