dc.contributor.author | Gragne, Ashenafi Seifu | |
dc.date.accessioned | 2015-06-02T13:13:07Z | |
dc.date.available | 2015-06-02T13:13:07Z | |
dc.date.issued | 2015 | |
dc.identifier.isbn | 978-82-326-0774-7 | |
dc.identifier.isbn | 978-82-326-0775-4 | |
dc.identifier.issn | 1503-8181 | |
dc.identifier.uri | http://hdl.handle.net/11250/284426 | |
dc.description.abstract | The consumption of electricity is growing to record levels due to population growth
and the global economic prosperity. The electricity generation projection for 2040 is
that 25% of the global electricity generation will come from renewable resources of
which 80% is attributed to hydro and wind power. Electricity generation during times
of high demand is well recompensed by the energy markets since the emergence of
electricity as a competitive commodity. This, along with maximizing the value of
water resources is a good incentive for establishing efficient hydropower reservoir
operation system. In this regard, hydrologic models enjoy wide application in
forecasting inflows into hydropower reservoirs and hence play an important role in
the operation and planning of hydropower systems. The inflow forecasts are yet far
from being perfect due to the numerous uncertainty sources that confront hydrologic
modelling. In this thesis, model updating procedures are developed and tested with
the Elspot (a day-ahead market of the Nordic Pool) as a focus of attention.
Furthermore, a skill to reduce the occurrence and accumulation of errors in the state
of a system without modifying the structure and parameterization of the existing
operational forecasting model was considered essential. Three updating procedures
were developed and tested through application to the Krinsvatn catchment using the
HBV model. Firstly a complementary modelling framework in which a data-driven
model captures the structure the conceptual operational model may be missing was
developed. Deterministic and probabilistic evaluations revealed that the procedure
successfully improved the skill of an hourly forecasting system for a lead-time up to
17 hours. Secondly, a filter updating procedure that recursively updates the gain on
the error forecasting scheme of the complementary modelling framework was
developed. Application to the same catchment showed that the filter updating
procedure extended the performance improvement of the complementary modelling
framework to a lead-time of 20 hours. In order to address the limitations of these
procedures that surfaced during applications to the study catchment, identifiability
of the snow routine parameters and sensitivity of the HBV model to the temperature
inputs were analysed using the GLUE and a pseudo-sensitivity assessment methods,
respectively. The assessment revealed that the precipitation phase partitioning and
snow melt/freeze processes were highly uncertain due to a combination of process
parameterisation and input data quality. It further revealed the importance of model
updating during the winter/spring season. On the basis of these findings a sequential
temperature updating procedure was developed. The aim of this procedure was to
correct the initial state of a forecasting system by correcting the temperature inputs.
This procedure was able to identify the input time steps in the past and quantify the
temperature corrections they needed. It resulted in a relative RMSE reduction of 21%
for the spring season. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | NTNU | nb_NO |
dc.relation.ispartofseries | Doctoral thesis at NTNU;2015:57 | |
dc.title | Updating Hydrologic Models for Improved Inflow Forecasts into Hydropower Reservoirs | nb_NO |
dc.type | Doctoral thesis | nb_NO |
dc.subject.nsi | VDP::Technology: 500::Environmental engineering: 610 | nb_NO |