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| Titel |
Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework |
| VerfasserIn |
A. S. Gragne, A. Sharma, R. Mehrotra, K. Alfredsen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 8 ; Nr. 19, no. 8 (2015-08-27), S.3695-3714 |
| Datensatznummer |
250120798
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| Publikation (Nr.) |
 copernicus.org/hess-19-3695-2015.pdf |
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| Zusammenfassung |
| Accuracy of reservoir inflow forecasts is instrumental for maximizing the
value of water resources and benefits gained through hydropower generation.
Improving hourly reservoir inflow forecasts over a 24 h lead time is
considered within the day-ahead (Elspot) market of the Nordic exchange
market. A complementary modelling framework presents an approach for
improving real-time forecasting without needing to modify the pre-existing
forecasting model, but instead formulating an independent additive or
complementary model that captures the structure the existing operational
model may be missing. We present here the application of this principle for
issuing improved hourly inflow forecasts into hydropower reservoirs over
extended lead times, and the parameter estimation procedure reformulated to
deal with bias, persistence and heteroscedasticity. The procedure presented
comprises an error model added on top of an unalterable constant parameter
conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error
model is established based on attributes of the residual time series from
the conceptual model. Besides improving forecast skills of operational
models, the approach estimates the uncertainty in the complementary model
structure and produces probabilistic inflow forecasts that entrain suitable
information for reducing uncertainty in the decision-making processes in
hydropower systems operation. Deterministic and probabilistic evaluations
revealed an overall significant improvement in forecast accuracy for
lead times up to 17 h. Evaluation of the percentage of observations
bracketed in the forecasted 95 % confidence interval indicated that the
degree of success in containing 95 % of the observations varies across
seasons and hydrologic years. |
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