Reduction of the Computational Cost of Tuning Methodology of a Simulator of a Physical System.

We propose a methodology for calibrating a physical system simulator and whose computational model represents its events in time series. The methodology reduces the search space of the fit parameters by exploring a database that contains stored historical events and their corresponding simulator fit parameters. We carry out the symbolic representation of the time series using ordinal patterns to classify the series, which allows us to search and compare by similarity on the stored data of the series represented. This classification strategy allows us to speed up the parameter search process, reduce the computational cost of the adjustment process and consequently improve energy cost savings. The experiences showed a reduction in the computational cost of 29% compared with our tuning methodology proposed in previous research.