With a growing population and ongoing industrialization, energy demand is rising on a global scale. Satisfying this demand in a sustainable way, while minimizing mankind’s impact on climate change, is a significant challenge for our and future generations. One of the options that is apt to make a difference is the use of low emission energy sources for heating purposes. One of these sources is Earth’s ubiquitous geothermal potential. Understanding this potential and its limitations is of utmost importance. Despite having a large impact on the operation and management of geothermal resources, the influence of uncertainty has not been studied extensively. Consequently, this paper discusses the role of uncertainty in detail. One of the main sources of uncertainty is the reservoirs’ physical reaction to production, as it allows forecasting of realistic exploitation values. To test how much information is needed to produce good forecasting results, an initial lumped parameter model fit is obtained to identify the complexity of the best fit for four low temperature reservoirs in Iceland. To simulate decreasing uncertainty, the operational data is cut into smaller portions. By gradually extending the data range and iterative fitting, a development of the coefficient of determination is visible; after ten seasonal cycles of data input, the model fit reaches a significant level of certainty. Furthermore, best fits do not show differences in model complexity for different levels of uncertainty. This time horizon can act as a stabilizing factor in the economic optimization and improve the accuracy of economic forecasting.

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