Many energy companies rely on natural resources to produce energy. They use
advanced models to estimate how much of those resources they have access
to, but if a model is to make an accurate estimation it needs to be accurately
calibrated.
There is little agreement in the science literature about what automatic calibration
method is the best one to use on numerical model. The Shuffled Complex
Evolution (SCE-UA) method is considered state of the art, and while it has been
over 20 years since it was developed it is still in use both for commercial purposes
and research.
We compared the SCE-UA method to three other methods that can potentially
be used for parameter optimization; Continuous Action Learning Automata(CALA),
Genetic Algorithms(GA) and a Monte Carlo Scheme. We implemented and configured
these methods to run an implementation of the HBV hydrological model.
The purpose of this was to see if the SCE-UA method was still the best one to
use compared to these more general methods.
We designed a test protocol and an evaluation method to compare the methods
on a level playing field. To be able to do this we had to research the characteristics
of the methods and how to configure them to work with the HBV model.
Our results conclusively showed the SCE-UA and Genetic Algorithm methods
giving the most accurate and efficient results. However, both their results were
so similar that we could not make a decisive conclusion of which one of them
was the best. We concluded that with our evaluation and test procedures they
produced roughly equal results. The CALA method came out worse than any of
the other methods
