Marc Jaxa-Rozen

Improving scenario discovery for handling heterogeneous uncertainties and multinomial classified outcomes

Scenario discovery is a novel model-based approach to scenario development in the presence of deep uncertainty. Scenario discovery frequently relies on the Patient Rule Induction Method (PRIM). PRIM identifies regions in the model input space that are highly predictive of producing model outcomes that are of interest. To identify these, PRIM uses a lenient hill climbing optimization procedure. PRIM struggles when confronted with cases where the uncertain factors are a mix of data types, and can be used only for binary classifications. We compare two more lenient objective functions which both address the first problem, and an alternative objective function using Gini impurity which addresses the second problem. We assess the efficacy of the modification using previously published cases. Both modifications are effective. The more lenient objective functions produce better descriptions of the data, while the Gini impurity objective function allows PRIM to be used when handling multinomial classified data. We compare three objective functions for PRIM in case of binary classified data.The more lenient objective functions outperform the less lenient objective functions.We introduce a new objective function for PRIM in case of multinomial classified data.We compare PRIM with the multinomial objective function to both CART, and sequential use of PRIM on each class separately.

The adoption and diffusion of common-pool resource-dependent technologies: The case of aquifer Thermal Energy Storage systems

The dynamics of technology diffusion and adoption have been studied extensively. There is broad agreement on the typical patterns that these dynamics follow, and models are readily available to forecast future technology adoption and diffusion. Most of the existing research, however, has not considered the dynamics of adoption and diffusion for technologies which rely on a common-pool resource (CPR). The sustainable exploitation of a common-pool resource imposes a natural limit on usage, and exploitation beyond this limit may deteriorate the resource. Aquifer Thermal Energy Storage (ATES) systems use aquifers in the subsurface for space heating and cooling. Although these systems may significantly reduce the energy consumption of buildings, over-adoption or exploitation of the aquifer will yield thermal interactions between systems, reducing their efficiencies. The aim of this paper is to provide insight into the adoption dynamics of ATES systems, notably in regards to the effects of overexploitation on subsequent adoption. We present a hybrid model that connects an agent-based model of ATES adoption with a geohydrologic model of the aquifer, including building energy flows. We explore the behavior of the model across a range of alternative parameterizations, identify typical dynamics, and analyze the conditions under which each of the dynamics occurs.

PyNetLogo: Linking NetLogo with Python

Methods for testing and analyzing agent-based models have drawn increasing attention in the literature, in the context of efforts to establish standard frameworks for the development and documentation of models. This process can benefit from the use of established software environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling software can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Python’s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogo’s features are demonstrated by controlling one of NetLogo’s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing.

Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques

Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input types. As an alternative, this work evaluates two decision tree-based methods to approximate GSA results: random forests, and Extra-Trees. These techniques are applicable with common sampling methods, and continuous or categorical inputs. The tree-based methods are compared to reference Sobol GSA and Morris screening techniques, for three cases: an Ishigami-Homma function, a H1N1 pandemic model, and the CDICE integrated assessment model. The Extra-Trees algorithm performs favorably compared to Morris elementary effects, accurately approximating the relative importance of Sobol total effect indices. Furthermore, Extra-Trees can estimate variable interaction importances using a pairwise permutation measure. As such, this approach could offer a user-friendly option for screening in models with inputs of mixed types.

Integrated building energy management using aquifer thermal energy storage (ATES) in smart thermal grids

Aquifer Thermal Energy Storage (ATES) is an innovative building technology that can be used to store thermal energy in natural subsurface formations [1, 4, 10]. In combination with a heat pump, ATES can reduce the energy demand of larger buildings by more than half, which has made the technology increasingly popular in northern Europe (see Figure 1). Furthermore, the climate and subsurface conditions required for ATES use can be found in areas across Europe, Asia and North America. By the middle of the century, roughly half of the worlds urban population is therefore expected to live in areas technically suitable for ATES [2].