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This paper examines the relationship between locus of control (LOC) and the demand for supplementary health insurance (SUPP). Drawing on longitudinal data from Germany, we document robust evidence that individuals internal LOC increases the take up of supplementary private health insurance (SUPP). We find that the effect of one standard deviation increase in the measure of internal LOC on the probability of SUPP purchase is equivalent to a 14 percent increase in household income. Second, we find that the positive association between self-reported health and SUPP becomes small and insignificant when we control for LOC. These results suggest that LOC might be an unobserved individual trait that can partly explain previously documented evidence of advantageous selection into SUPP. Third, we find comparable results using data from Australia, which enhances the external validity of our results. [ABSTRACT FROM AUTHOR]

Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data. [ABSTRACT FROM AUTHOR]

The estimation of the electrical power output and energy yield of high concentration photovoltaic (HCPV) modules is a hard task because of the many parameters involved. In this work, we propose the usage of an Artificial Neural Networks (ANN) method to estimate the maximum power output of a FLATCON® CPV-module manufactured by Fraunhofer ISE. The advantage of the ANN is that it is a part of the Artificial Intelligence Deep Learning domain, which makes it easy to model complex systems. A detailed knowledge about the underlying module technology is not necessary. In this work, various meteorological parameters are tested as inputs of the ANN including spectral matching ratios for considering the spectral variation of the solar irradiance to evaluate the maximum power output of the HCPV module under study. Eleven scenarios considering different combinations of input parameters have been investigated. It is demonstrated that the ANN gives excellent results and allows for an accurate prediction of the HCPV module's instantaneous power output with only a few amount of data used on training the ANN. The model has been tested using the measured data set of a FLATCON® CPV-module located on the rooftop of the Fraunhofer ISE in Freiburg, Germany. The module has been electrically characterized outdoors in the period from April 2013 to April 2014. The accuracy of the proposed ANN approach was analyzed using this measurement data in combination with error metrics. It was found that the best agreement between the measured and the predicted maximum power output was achieved when using direct normal irradiance, wind speed, ambient temperature and spectral matching ratios as input. The normalized root mean square error for the maximum power output over one year is found to be between 2.2 and 4.6%. The deviation of the modelled energy yield to the measured one is in the range of 0.2–2.2%. • We use ANN method to predict the maximum power output of a FLATCON® CPV-module. • The accuracy of model used was validated through measurements and metrics errors. • The spectral effect of DNI through the use of SMRs has improved the model performance. • The knowledge of module technology is not necessary for performance prediction. • Only small amount of data are needed for performance prediction using this approach. Short Abstract: This paper uses the artificial Neural Networks model to estimate the electrical power and the energy yield of high concentration photovoltaic modules. The model has been verified using a whole year measurement data of power output of FLATCON CPV Module manufactured by Fraunhofer ISE, Germany. A best agreement between the measured and computed energy output was found trough the analysis of eleven scenarios where different input parameters were combined. [ABSTRACT FROM AUTHOR]

Solar power integration has shown a significant growth in many power systems during the last decade. The intermittent nature of solar irradiance tends to vary the amount of solar power in the system and an accurate solar power forecasting method can be used to tackle this in power system planning and operation. In this paper, authors have proposed a generalized ensemble model integrating deep learning techniques to generate accurate solar power forecasts for 21 solar photovoltaic facilities located in Germany. Most important weather parameters for solar power generation are selected through a feature selection process. In addition, a weather classification approach is used to cluster the dataset and for each cluster, a separate ensemble algorithm is assigned. Finally, considering the prediction errors in each cluster, a novel ensemble model is developed. The proposed models are evaluated using root mean square error and results are compared with single machine learning techniques and available forecasting models in the literature. Compared to deep belief network, support vector regression and random forest regression models, the proposed ensemble model with cloud classification reduces RMSE error by 10.49%, 7.78%, and 7.95% respectively. Results show that the weather classification approach reduces the forecasting error by a considerable margin and the proposed ensemble model provides a better forecasting accuracy than single machine learning methods. [ABSTRACT FROM AUTHOR]

The identification of buried soil horizons in agricultural landscapes helps to quantify sediment budgets and erosion‐related carbon dynamics. High‐resolution mapping of buried horizons using conventional soil surveys is destructive and time consuming. Geoelectrical sensors can offer a fast and non‐destructive alternative for determining horizon positions and properties. In this paper, we compare the suitability of several geoelectrical methods for measuring the depth to buried horizons (Apb, Ahb and Hab) in the hummocky ground moraine landscape of northeastern Germany. Soil profile descriptions were developed for 269 locations within a 6‐ha experimental field "CarboZALF‐D". A stepwise linear discriminant analysis (LDA) estimated the lateral position of the buried horizons using electromagnetic induction data and terrain attributes. To predict the depth of a buried horizon, multiple linear regression (MLR) was used for both a 120‐m transect and a 0.2‐ha pseudo‐three‐dimensional (3D) area. At these scales, apparent electrical conductivity (ECa), electrical resistivity (ER) and terrain attributes were used as independent variables. The LDA accurately predicted Apb‐ and Ahb‐horizons (a correct classification of 93%). The LDA of the Hab‐horizon had a misclassification of 24%, which was probably related to the smaller test set and the higher depth of this horizon. The MLR predicted the depth of the Apb‐, Ahb‐ and Hab‐horizons with relative root mean square errors (RMSEs) of 7, 3 and 13%, respectively, in the pseudo‐3D area. MLR had a lower accuracy for the 2D transect compared to the pseudo‐3D area. Overall, the use of LDA and MLR has been an efficient methodological approach for predicting buried horizon positions. Highlights: The suitability of geoelectrical measurements for digital modelling of diagnostic buried soil horizons was determined.LDA and MLR were used to detect multiple horizons with geoelectrical devices and terrain attributes.Geoelectrical variables were significant predictors of the position of the target soil horizons.The use of these tested digital technologies gives an opportunity to develop high‐resolution soil mapping procedures. [ABSTRACT FROM AUTHOR]

• Data-driven train delay forecasting using machine learning at scale. • Using operational data from Deutsche Bahn for 25,000 trains daily. • Online method combines machine learning and simulation. • Key pre-processing steps handled automatically. A large-scale ensemble prediction model to predict train delays is presented. The ensemble model uses a disparate set of models, two statistical and one simulation-based to generate forecasts of train delays. The first statistical model is a context-aware random forest that accounts for network traffic states, such as likely stretch conflicts and current headway's, exogenous weather, event, and work zone information. The second model is a kernel regression that captures train-specific dynamics. A mesoscopic simulation model that accounts for travel and dwell time variations as well as inferred track occupation conflicts, train connections and rolling stock rotations, is additionally considered. The models have been used in a proof of concept to forecast delays for nationwide passenger services network of Deutsche Bahn, which operates roughly 25,000 trains daily in Germany. Results demonstrate a 25% improvement potential in forecast correctness (fraction of predictions within one minute) and 50% reduction in root mean squared errors compared to the published schedule. The paper describes the models along with the big data challenges that were addressed in data storage, feature and model building, and computation. [ABSTRACT FROM AUTHOR]

• The performance of on-line vis-NIR for estimating soil organic carbon was evaluated. • Predictions for three datasets single field, UK with spiking, and UK were compared. • Random forest (RF) regression and spiking technique were used. • The best results obtained with the spiked UK dataset. Accurate on-line visible and near infrared (vis-NIR) spectroscopy prediction of soil organic carbon (OC) is essential for food security and environmental management. This paper aims at using on-line vis-NIR spectra coupled with random forest (RF) modelling approach for the prediction of soil organic carbon (OC), comparing between single field (SF), non-spiked UK multiple-field (NSUK) and spiked UK multiple-field (SUK) calibration models. Fresh soil samples collected from 6 fields in the UK (including two target fields) were scanned with a fibre-type vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. After dividing spectra into calibration and independent validation sets, RF was run on the calibration set to develop calibration models for OC for the three studied datasets. Results showed that the model prediction performance depends on the dataset used and varies between fields. Less accurate prediction performance was obtained for the on-line prediction compared to laboratory (samples scanned in the laboratory under non-mobile measurement) prediction, and for non-spiked models compared to spiked models. The best model performance in both laboratory and on-line predictions was obtained when samples from the SF were spiked into the UK samples, with coefficients of determination (R2) values of 0.80 to 0.84 and 0.74 to 0.75, root mean square error of prediction (RMSEP) values of 0.14% and 0.17 to 0.18%, and ratio of prediction deviation (RPD) values of 2.30 to 2.5 and 1.98 to 2.04, respectively. Therefore, these results suggest that RF modelling approach when coupled with spiking provides high prediction performance of OC under both non-mobile laboratory and on-line field scanning conditions. [ABSTRACT FROM AUTHOR]

Boron element is widely distributed in different geologic bodies, and there are important geo-chemical applications in earth science. Halite is a common mineral found in sediment basin. However there is no good method to accurately measure the boron content in halite, which is mainly because Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES) and Inductively Coupled Plasma Mass Spectrometer (ICP-MS) are limited by the high salt matrix interference and the instrument detection limit. Thus enriching the boron element and removing the matrix interference are necessary before the measuring. In this paper, Amberlite IRA 743 boron-specific resin was applied to enrich the boron element and remove most of the high-salt matrix. The strong acid cation resin (Dowex 50 W×8, 200-400 mesh, USA) and weak-base anion resin (Ion Exchanger II, Germany) were mixed with equal volume, which could remove the foreign ions completely: meanwhile, the relative content of boron in the solution reached above 98%, and the recoveries ranged from 97.8% to 104%. 208.900 nm was chosen as the detection wavelength for ICP-OES, and the detection identification and quantification limits were 0.006 mg·L−1 and 0.02 mg·L−1, respectively. 11B was chosen as the measuring element for ICP-MS, and the detection identification and quantification limits were severally 0.036 mg·L−1 and 0.12 mg·L−1. The relative standard deviations ranged from 1.4% to 3.4% through six replicates under different salinities. Therefore, the process could be regarded as a feasible method to measure boron content in halite by ICP-OES and ICP-MS. [ABSTRACT FROM AUTHOR]

This paper examines the responsiveness of bond yields to changes in debt supply. The preferred-habitat theory predicts a positive relation between the term spread and relative supply of longer term debt, and that this relation is stronger when risk aversion is high. To capture this effect, a time-varying coefficient model is introduced and applied to German bond data. The results support the theoretical predictions and indicate substantial time variation: under high risk aversion, yield spreads react about three times more strongly than when risk aversion is low. The accumulated response of term spreads to a one standard deviation change in debt supply ranges between 4 and 46 basis points. [ABSTRACT FROM AUTHOR]

• This paper explores probabilistic extensions to Deep Learning algorithms and their application on energy time series forecasting. • The methods tested are concrete Dropouts, deep Ensembles, bayesian neural Networks, deep gaussian Processes, and functional neural processes. • The methods are evaluated in terms of calibration, sharpness, and how well they indicate a lack of knowledge (Epistemic Uncertainty). • Good epistemic uncertainty estimates were given using functional neural Processes, deep Ensembles, and deep gaussian processes methods, while concrete dropouts and bayesian neural networks needed tunning for better performance. • The application of the methods was feasible for energy time series forecasting, and they produced good estimates of their confidence in a forecast. Deep Learning methods are widely successful and continue to be applied in new fields, including forecasting for energy time series. However, they lack an indication of their confidence in predictions, which helps evaluate, interpret, and improve the forecasting. This paper explores probabilistic extensions to Deep Learning algorithms and their application on energy time series forecasting. The methods tested are Concrete Dropouts, Deep Ensembles, Bayesian Neural Networks, Deep Gaussian Processes, and Functional Neural Processes. For evaluation, two load forecasting scenarios are considered: 1-hour-ahead and 24-hour-ahead load forecasting. The methods are evaluated in terms of calibration, sharpness, and how well they indicate a lack of knowledge (Epistemic Uncertainty). As a reference, simple Neural Network and Quantile Regression models are used. Also, the ARIMA model and Persistence model are implemented to compare the results. Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Continuous Ranked Probability Score (CRPS) are computed to measure the error performance. Based on endogenous load time series in Germany, the results showed that Concrete Dropout, Deep Ensembles, and Bayesian Neural Networks performed similarly well and better than the reference methods. They also indicated that the simpler Concrete Dropout and Deep Ensembles have an advantage in both speed and convergence time. Good Epistemic Uncertainty estimates were given using Functional Neural Processes, Deep Ensembles, and Deep Gaussian Processes methods, while Concrete dropouts and Bayesian Neural Networks needed tunning for better performance. The application of the methods was feasible for energy time series forecasting, and they produced good estimates of their confidence in a forecast. [ABSTRACT FROM AUTHOR]

• Analysis of hydrothermal potential in German municipalities. • Optimisation of simultaneous geothermal heat and electricity generation. • Drilling depth and hydrothermal temperature are implemented endogenously. • Integration of geothermal plants in a holistic energy system optimisation. • Geothermal plants reveal a potential for cost reduction in off-grid municipalities. A growing number of German municipalities are striving for energy autonomy. Geothermal plants are increasingly constructed in municipalities in order to exploit the high hydrothermal potential. This paper analyses the potential contribution of simultaneous geothermal power and heat generation in German municipalities to achieving energy autonomy. A linear regression estimates the achievable hydrothermal temperatures and the required drilling depths. Technical restrictions and cost estimations for geothermal plants are implemented within an existing linear optimisation model for municipal energy systems. Novel modelling approaches, such as optimisation with variable drilling depths, are developed. The new approach is validated with data from existing geothermal plants in Germany, demonstrating a Root Mean Squared Error of about 15%. Eleven scenarios show that achieving energy autonomy is associated with at least 4% additional costs, compared to scenarios without it. The crucial role of geothermal plants in providing base load heat and power to achieve energy autonomy is demonstrated. The importance of simultaneous modelling of electricity and heat generation in geothermal plants is also evident, as district heating plants reduce the costs, especially in municipalities with high hydrothermal potential. Further work should focus on the optimal spatial scale of the system boundaries and the impact of the temporal resolution of the analysis on the costs for achieving energy autonomy. [ABSTRACT FROM AUTHOR]