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Regression of data generated in simulations or experiments has importantimplications in sensitivity studies, uncertainty analysis, and predictionaccuracy. Depending on the nature of the physical model, data points may not beevenly distributed. It is not often practical to choose all points forregression of a model because it doesn't always guarantee a better fit. Fitnessof the model is highly dependent on the number of data points and thedistribution of the data along the curve. In this study, the effect of thenumber of points selected for regression is investigated and various schemesaimed to process regression data points are explored. Time series data i.e.,output varying with time, is our prime interest mainly the temperature profilefrom enhanced geothermal system. The objective of the research is to find abetter scheme for choosing a fraction of data points from the entire set tofind a better fitness of the model without losing any features or trends in thedata. A workflow is provided to summarize the entire protocol of datapreprocessing, regression of mathematical model using training data, modeltesting, and error analysis. Six different schemes are developed to processdata by setting criteria such as equal spacing along axes (X and Y), equaldistance between two consecutive points on the curve, constraint in the angleof curvature, etc. As an example for the application of the proposed schemes, 1to 20% of the data generated from the temperature change of a typicalgeothermal system is chosen from a total of 9939 points. It is shown that thenumber of data points, to a degree, has negligible effect on the fitted modeldepending on the scheme. The proposed data processing schemes are ranked interms of R2 and NRMSE values.

Data Processing Protocol for Regression of Geothermal Times Series with Uneven Intervals