Model predictive control (MPC) is a promising approach for optimizing the performance of borehole heat exchangers (BHEs) in ground-source heat pump systems. The central element of MPC is the forward model that predicts the thermal dynamics in the ground. In this work, we validate the prediction accuracy of four BHE modeling approaches against real-world measurement data across various operational events and timescales. We simulate the fluid temperature leaving a BHE using a fully discretized 3-D numerical model, a resistance–capacitance model, a g-function model, and a hybrid model. The simulated temperatures are compared to measured temperatures using three validation metrics that quantify temperature offset, noise, and accuracy. The main reason for a mismatch between measured and modeled temperatures is a temperature offset of the simulated temperature. To remove this effect, the models were calibrated for their most sensitive parameter, the ground temperature, and their prediction accuracy over 4 years was evaluated. Thereby, model calibration seems to be a viable solution to account for an unknown load history. The results show that the resistance–capacitance model provides decent predictions in the short term and the g-function model in the long term. However, both models are strongly dependent on accurate calibration. The hybrid model provides the most accurate short and long-term predictions and is less dependent on calibration. Still, its integration into optimization syntax poses challenges compared to the other models. Although not yet applied in model predictive control, the hybrid model stands out as a promising choice for optimizing BHE field operations across various timescales.

Ground-source heat pumps with borehole heat exchangers (BHE) are an efficient and sustainable option to heat and cool buildings. The design and performance of BHEs strongly depend on the thermal conductivity of the subsurface. Thus, the first step in BHE planning is often assisted by a map representing the thermal conductivity of a region created from existing data. Such estimates have high uncertainty, which is rarely quantified. In addition, different methods for estimating thermal conductivity are used, for example, by the German federal states, resulting in incomparable estimates. To enable a consistent thermal conductivity estimation across state or country borders, we present a workflow for automatically estimating the thermal conductivity and its uncertainty up to user-defined BHE lengths. Two methods, which assess the thermal conductivity on different scales, are developed. Both methods are (1) based on subsurface data types which are publicly available as open-web services, and (2) account for thermal conductivity uncertainty by estimating its lowest, mean, and maximum values. The first method uses raster data, e.g., of surface geology and depth to groundwater table, and provides a large-scale estimate of the thermal conductivity, with high uncertainty. The second method improves the estimation for a small, user-defined target area by calculating the thermal conductivity based on the available borehole data in that area. The presented approach’s novelty is a web-based geodata infrastructure that seamlessly connects data provision and calculation processes, with a geoportal as its central user interface. To demonstrate the approach, we use data from the federal state of Hamburg and compare the results of two target areas with the thermal conductivity estimation by the Geological Survey of Hamburg. Depending on the selected region, differences between the two estimates can be considerable (up to 1.2 $\mathrm{W} {\mathrm{m}}^{-1} {\mathrm{K}}^{-1}$). The differences are primarily due to the selection of the thermal property database and the consideration of wet and dry rock. The results emphasize the importance of considering and communicating uncertainty in geothermal potential estimates.

Ground-source heat pumps (GSHPs) coupled with borehole heat exchangers (BHEs) are energy-efficient technologies for heating and cooling buildings. However, these systems often fail to operate at their full potential due to discrepancies between the assumptions made during the design phase and the actual conditions during operation. To enhance overall GSHP performance, it is crucial to collect and analyze long-term monitoring data from operating BHE fields. To our knowledge, no long-term, high-resolution dataset of double U-tube BHEs is currently publicly available. Additionally, most studies typically monitor only the inlet and outlet of the entire ground heat exchanger rather than individual BHEs, hindering detailed performance analysis. With this data descriptor, we present a 6-year dataset from a BHE field comprising 40 BHEs, each with sensors for volume flow and inlet/outlet temperatures, recorded every 30 seconds. We believe this dataset will enhance understanding of individual BHE performance, provide validation for BHE models, and thus support better GSHP design and operation.

Quantitative uncertainty analysis, 2-D and 3-D modeling of the subsurface, as well as their visualization form the basis for decision making in exploration, nuclear waste storage and seismic hazard assessment. Methods such as cross-section balancing are well established and yield plausible kinematic scenarios. However, they are based on geological data with errors and subject to human biases. Additionally, kinematic models do not provide a quantitative measure of the uncertainty of structures at depth. New 3-D modeling approaches have emerged that use computational interpolation, which are less dependent on human biases. Probabilistic extensions enable the quantification of uncertainties for the modeled structures. However, these approaches do not provide information on the time evolution of structures. Here, we compare classical cross-section balancing (2-D, kinematic modeling) with 3-D computational modeling to pave the way towards a solution that can bridge between these approaches. We show the strengths and weaknesses of both approaches, highlighting areas where probabilistic modeling can possibly add quantitative structural uncertainty information to improve section balancing. On the other hand, we show where probabilistic modeling still falls short of being able to cover the observed geometric complexities. We ultimately discuss how a workflow that iteratively combines results of the approaches can improve structural and kinematic constraints. As an example, we use the fold-and-thrust belt of the northern Alpine Foreland, the so-called Subalpine Molasse, focusing on the Hausham Syncline (Bavaria) and adjacent areas. We take advantage of the fact that here the stratigraphy as well as the tectonic history are well constrained. We show that shortening within the syncline progressively increases from west to east, independent from structural uncertainties. Two equally viable models can explain this. First, strain in the west is accommodated underneath the syncline in a triangle zone that progressively tapers out, or second, the strain difference is accommodated in more internal units. This highlights the importance of introducing uncertainty modeling also in kinematic restorations, as it enables identifying key regions, where different hypotheses can be tested.

In Table 3 of this article, the Ground average volumetric heat capacity and Grout volumetric heat capacity were incorrectly given as '2.3 × 10-6 ' and '1.0 × 10-6 ' but should have been '2.3 × 10 6 ' and '1.0 × 10 6 ' , respectively.