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This bibliography lists 155 books, articles, monographs, and other studies on operations analysis in education. The criteria for inclusion are that the study be quantitative and analytical and use techniques of operations research, systems analysis, management science, program planning and budgeting, benefit analysis, cost analysis, benefit-cost analysis, cost effectiveness analysis, multivariate statistical methods, economics, or computer science. A list of six bibliographies which include most of the studies cited and a list of 38 Technical Notes of the Division of Operations Analysis that are available through ERIC are appended. This document replaces ED 014 129. (HW)

Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.

Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims., Comment: 11 pages, 8 figures, appendix

Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere. A downside of numerical weather prediction is that it is lacking the ability for short-term forecasts using the latest available information. By using a data-driven neural network approach we show that it is possible to produce an accurate precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient convolutional neural networks-based on the well known UNet architecture equipped with attention modules and depthwise-separable convolutions. We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France. The experimental results show that in terms of prediction performance, the proposed model is comparable to other examined models while only using a quarter of the trainable parameters., Comment: 9 pages, 4 figures

Partial shading is widely considered to be a limiting factor in the performance of photovoltaic (PV) systems applied in urban environments. Modern system architectures combined with per module deployment of power electronics have been used to improve performance especially at heterogeneous irradiance conditions. In this work another approach is used to combine modern system architecture with alternate module designs. The granularity of cell groups in PV modules is investigated together with the so-called Tessera concept, in which single cells are cut in 16 parts. Typical meteorological year yield calculations show that these alternate module designs in combination with modern system architectures can retrieve up to half the shading losses compared to standard modules and string inverters under identical shading conditions.

Wind speed prediction and forecasting is important for various business and management sectors. In this paper, we introduce new models for wind speed prediction based on graph convolutional networks (GCNs). Given hourly data of several weather variables acquired from multiple weather stations, wind speed values are predicted for multiple time steps ahead. In particular, the weather stations are treated as nodes of a graph whose associated adjacency matrix is learnable. In this way, the network learns the graph spatial structure and determines the strength of relations between the weather stations based on the historical weather data. We add a self-loop connection to the learnt adjacency matrix and normalize the adjacency matrix. We examine two scenarios with the self-loop connection setting (two separate models). In the first scenario, the self-loop connection is imposed as a constant additive. In the second scenario a learnable parameter is included to enable the network to decide about the self-loop connection strength. Furthermore, we incorporate data from multiple time steps with temporal convolution, which together with spatial graph convolution constitutes spatio-temporal graph convolution. We perform experiments on real datasets collected from weather stations located in cities in Denmark and the Netherlands. The numerical experiments show that our proposed models outperform previously developed baseline models on the referenced datasets. We provide additional insights by visualizing learnt adjacency matrices from each layer of our models., Comment: 10 pages, 8 figures

Neuroimaging techniques have shown to be valuable when studying brain activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), and different deep learning models to perform brain decoding. Specifically, we investigate to which extent one can infer the task performed by a subject based on its MEG data. In order to capture the most relevant features of the signals, self and global attention are incorporated into our models. The obtained results show that the inclusion of attention improves the performance and generalization of the models across subjects.

In some machine learning applications, obtaining data on the most predictive features is costly, but other features are readily available. Recently, first active learning approaches for this Actively Selecting Classification Features problem (ASCF) have been proposed. In this paper, we introduce a Python package that provides a framework for ASCF, including implementations of a supervised and an unsupervised selection approach, as well as a framework for performing experimental evaluations. This framework has been used in recent publications in the context of neuroimaging research on mental disorders, where its usefulness has been demonstrated in a simulated study design with MRI data.

Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcasting has recently gained plenty of attention. In this paper, we treat the nowcasting problem as an image-to-image translation problem using satellite imagery. We introduce Broad-UNet, a novel architecture based on the core UNet model, to efficiently address this problem. In particular, the proposed Broad-UNet is equipped with asymmetric parallel convolutions as well as Atrous Spatial Pyramid Pooling (ASPP) module. In this way, The the Broad-UNet model learns more complex patterns by combining multi-scale features while using fewer parameters than the core UNet model. The proposed model is applied on two different nowcasting tasks, i.e. precipitation maps and cloud cover nowcasting. The obtained numerical results show that the introduced Broad-UNet model performs more accurate predictions compared to the other examined architectures., 9 pages, 11 figures

Life science experiments that employ automated technologies, such as high-content screens, frequently produce large datasets that require substantial amounts of preprocessing before analysis can be carried out. Standardization of this preprocessing becomes impossible as the dataset size increases if there are manual steps involved. Virtually no standards for preprocessing currently exist and few user-friendly tools are available that allow the cleaning of data files in a simple and transparent manner while also allowing for reproducibility. We demonstrate in a publicly available R package, PurifyR, how preprocessing steps can be streamlined and automated. PurifyR supports multithreading and the standardization of large-matrix preprocessing. These steps provide transparent and reproducible preprocessing for matrix-oriented datasets. The PurifyR package is open source and can be downloaded from github.

Educational tests can be used to estimate pupils' abilities and thereby give an indication of whether their school type is suitable for them. However, tests in education are usually conducted for each content area separately which makes it difficult to combine these results into one single school advice. To help with school advice, we provide a comparison between both domain-specific and domain-agnostic methods for predicting school types. Both use data from a pupil monitoring system in the Netherlands, a system that keeps track of pupils' educational progress over several years by a series of tests measuring multiple skills. A domain-specific item response theory (IRT) model is calibrated from which an ability score is extracted and is subsequently plugged into a multinomial log-linear regression model. Second, we train domain-agnostic machine learning (ML) models. These are a random forest (RF) and a shallow neural network (NN). Furthermore, we apply case weighting to give extra attention to pupils who switched between school types. When considering the performance of all pupils, RFs provided the most accurate predictions followed by NNs and IRT respectively. When only looking at the performance of pupils who switched school type, IRT performed best followed by NNs and RFs. Case weighting proved to provide a major improvement for this group. Lastly, IRT was found to be much easier to explain in comparison to the other models. Thus, while ML provided more accurate results, this comes at the cost of a lower explainability in comparison to IRT.

In teleoperations, robots are generally used because related tasks are too dangerous, uncomfortable or impossible for humans to perform. When using augmented reality to control robotic limbs in such teleoperations, it is essential to understand how these extra virtual limbs are experienced. In particular, the relationship between the embodiment experience of the user and relevant outcomes such as task performance must be examined. In this article, we study the relationship between experienced embodiment of a supernumerary virtual arm that acts alongside a user’s two real arms, and their task performance in augmented reality. Specifically, we compare how well users can trace a virtual half ring placed just outside of personal space using their virtual arm in a condition where there is expected to be low embodiment (a floating disconnected hand) and a condition where there is expected to be high embodiment (a connected arm and hand). Embodiment is measured quantitatively through skin conductance response and qualitatively through ownership, agency, and self-location questionnaires. Performance is measured in terms of tracing precision. The results show positive correlations between subjective ownership and agency, and agency and performance, but no correlation between subjective or objective ownership and performance. Also, ownership ratings were low overall, while the agency ratings were significantly higher for the disconnected hand condition than the connected arm condition, as was performance. Notably, the presence of the virtual arm evoked incorrect expectations of the movement capabilities of the arm, which may have contributed to an overall preference for the unrealistic disconnected hand over the more realistic connected arm in this particular task. Our results imply that methods to increase performance in various teleoperations can indeed be found in the experience of embodiment: not necessarily directly through ownership, but through ownership mediated by agency.

In this paper we consider the train unit shunting problem extended with service task scheduling. This problem originates from Dutch Railways, which is the main railway operator in the Netherlands. Its urgency stems from the upcoming expansion of the rolling stock fleet needed to handle the ever-increasing number of passengers. The problem consists of matching train units arriving on a shunting yard to departing trains, scheduling service tasks such as cleaning and maintenance on the available resources, and parking the trains on the available tracks such that the shunting yard can operate conflict-free. These different aspects lead to a computationally extremely difficult problem, which combines several well-known NP-hard problems. In this paper, we present the first solution method covering all aspects of the shunting and scheduling problem. We describe a partial order schedule representation that captures the full problem, and we present a local search algorithm that utilizes the partial ordering. The proposed solution method is compared with an existing mixed integer linear program in a computational study on realistic instances provided by Dutch Railways. We show that our local search algorithm is the first method to solve real-world problem instances of the complete shunting and scheduling problem. It even outperforms current algorithms when the train unit shunting problem is considered in isolation, that is, without service tasks. Although our method was developed for the case of the Dutch Railways, it is applicable to any shunting yard or service location, irrespective of its layout, that uses self-propelling train units and that does not have to handle passing trains.

Density estimation is a versatile technique underlying many data mining tasks and techniques,ranging from exploration and presentation of static data, to probabilistic classification, or identifying changes or irregularities in streaming data. With the pervasiveness of embedded systems and digitisation, this latter type of streaming and evolving data becomes more important. Nevertheless, research in density estimation has so far focused on stationary data, leaving the task of of extrapolating and predicting density at time points outside a training window an open problem. For this task, Temporal Density Extrapolation (TDX) is proposed. This novel method models and predicts gradual monotonous changes in a distribution. It is based on the expansion of basis functions, whose weights are modelled as functions of compositional data over time by using an isometric log-ratio transformation. Extrapolated density estimates are then obtained by extrapolating the weights to the requested time point, and querying the density from the basis functions with back-transformed weights. Our approach aims for broad applicability by neither being restricted to a specific parametric distribution, nor relying on cluster structure in the data.It requires only two additional extrapolation-specific parameters, for which reasonable defaults exist. Experimental evaluation on various data streams, synthetic as well as from the real-world domains of credit scoring and environmental health, shows that the model manages to capture monotonous drift patterns accurately and better than existing methods. Thereby, it requires not more than 1.5-times the run time of a corresponding static density estimation approach., Comment: Accepted for publication in Data Mining and Knowledge Discovery, Special issue of the ECML/PKDD 2019 Journal Track, Springer, 2019

Analysing correlations between streams of events is an important problem. It arises for example in Neurosciences, when the connectivity of neurons should be inferred from spike trains that record neurons’ individual spiking activity. While recently some approaches for inferring delayed synaptic connections have been proposed, they are limited in the types of connectivities and delays they are able to handle, or require computation-intensive procedures. This paper proposes a faster and more flexible approach for analysing such delayed correlated activity: a statistical Analysis of the Connectivity of spiking Events (ACE), based on the idea of hypothesis testing. It first computes for any pair of a source and a target neuron the inter-spike delays between subsequent source- and target-spikes. Then, it derives a null model for the distribution of inter-spike delays for uncorrelated neurons. Finally, it compares the observed distribution of inter-spike delays to this null model and infers pairwise connectivity based on the Pearson’s \(\chi ^2\) test statistic. Thus, ACE is capable to detect connections with a priori unknown, non-discrete (and potentially large) inter-spike delays, which might vary between pairs of neurons. Since ACE works incrementally, it has potential for being used in online processing. In an experimental evaluation, ACE is faster and performs comparable or better than four baseline approaches, in terms of AUPRC (reported here), F1, and AUROC (reported on our website), for the majority of the 11 evaluated scenarios.

The single-unit commitment problem (1UC) is the problem of finding a cost optimal schedule for a single generator given a time series of electricity prices subject to generation limits, minimum up- and downtime and ramping limits. In this paper we present two efficient dynamic programming algorithms. For each time step we keep track of a set of functions that represent the cost of optimal schedules until that time step. We show that we can combine a subset of these functions by only considering their minimum. We can construct this minimum either implicitly or explicitly. Experiments show both methods scale linear in the amount of time steps and result in a significant speedup compared to the state-of-the-art for piece-wise linear as well as quadratic generation cost. Therefore using these methods could lead to significant improvements for solving large scale unit commitment problems with Lagrangian relaxation or related methods that use 1UC as subproblem.

Multi-objective spatial optimization problems require spatial data input that can contain uncertainties. Via the validation of constraints and the computation of objective values this uncertainty propagates to the Pareto fronts. Here, we develop a method to quantify the uncertainty in Pareto fronts by finding the extreme lower and upper bound of the range of optimal values in the objective space, i.e. the Pareto interval. The method is demonstrated on a land use allocation problem with initial land use (for objectives and constraints) and soil fertility (for one objective) as uncertain input data. Pareto intervals resulting from uncertain land use data were wide and irregularly shaped, whereas the ones from uncertain soil data were narrow and regularly shaped. Furthermore, in some objective-space regions, optimal land use patterns remained relatively stable under uncertainty, while elsewhere they were clouded. This information can be used to select solutions robust to spatial input data uncertainty.

Remote control of robots generally requires a high level of expertise and may impose a considerable cognitive burden on operators. A sense of embodiment over a remote-controlled robot might enhance operators? task performance and reduce cognitive workload. We want to study the extent to which different factors affect embodiment. As a first step, we aimed to validate the cross-modal congruency effect (CCE) as a potential objective measure of embodiment under four conditions with different, a priori expected levels of embodiment, and by comparing CCE scores with subjective reports. The conditions were (1) a real hand condition (real condition), (2) a real hand seen through a telepresence unit (mediated condition), (3) a robotic hand seen through a telepresence unit (robot condition), and (4) a human-looking virtual hand seen through VR glasses (VR condition). We found no unambiguous evidence that the magnitude of the CCE was affected by the degree of visual realism in each of the four conditions. We neither found evidence to support the hypothesis that the CCE and embodiment score as assessed by the subjective reports are correlated. These findings raise serious concerns about the use of the CCE as an objective measure of embodiment.

We describe a practical situation in which the application of forecasting models could lead to energy efficiency and decreased risk in water level management. The practical challenge of forecasting water levels in the next 24 h and the available data are provided by a dutch regional water authority. We formalized the problem as conditional forecasting of hydrological time series: the resulting models can be used for real-life scenario evaluation and decision support. We propose the novel Encoder/Decoder with Exogenous Variables RNN (ED-RNN) architecture for conditional forecasting with RNNs, and contrast its performance with various other time series forecasting models. We show that the performance of the ED-RNN architecture is comparable to the best performing alternative model (a feedforward ANN for direct forecasting), and more accurately captures short-term fluctuations in the water heights.

When decisions must be based on incomplete (coarsened) observations and the coarsening mechanism is unknown, a minimax approach offers the best guarantees on the decision maker’s expected loss. Recent work has derived mathematical conditions characterizing minimax optimal decisions, but also found that computing such decisions is a difficult problem in general. This problem is equivalent to that of maximizing a certain conditional entropy expression. In this work, we present a highly efficient algorithm for the case where the coarsening mechanism can be represented by a tree, whose vertices are outcomes and whose edges are coarse observations.

Railway systems occasionally get into a state of being out-of-control, meaning that barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing disruption management techniques cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework combining techniques from complexity science and operations research, aiming at reducing the impact of these situations and—if possible—avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations, (ii) isolating a specific region such that delay stops propagating, and (iii) the application of decentralized decision making, more suited for information-sparse out-of-control situations.

The goal of entity matching in knowledge graphs is to identify sets of entities that refer to the same real-world object. Methods for entity matching in knowledge graphs, however, produce a collection of pairs of entities claimed to be duplicates. This collection that represents the sameAs relation may fail to satisfy some of its structural properties such as transitivity. We show that an ad-hoc enforcement of transitivity on the set of identified entity pairs may decrease precision. We therefore propose a methodology that starts with a given similarity measure, generates a set of entity pairs, and applies cluster editing to enforce transitivity, leading to overall improved performance.

In the domain of the Dutch cultural heritage various data sets describe different aspects of life during the Dutch Golden Age. These data sets, in the form of RDF graphs, use different standards and contain noise in the values of literal nodes, such as misspelled names and uncertainty in dates. The Golden Agents project aims at answering queries about the Dutch Golden ages using these distributed and independently maintained data sets. A problem in this project, among many other problems, is the identification of persons who occur in multiple data sets but under different URI's. This paper aims to solve this specific problem and generate a linkset, i.e. a set of pairs of URI's which are judged to represent the same person. We use domain knowledge in the application of an existing node context generation algorithm to serve as input for GloVe, an algorithm originally designed for embedding words. This embedding is then used to train a classifier on pairs of URI's which are known duplicates and non-duplicates. Using just the cosine similarity between URI-pairs in embedding space for prediction, we obtain a simple classifier with an Fh-score of around 0.85, even when very few training examples are provided. On larger training sets, more complex classifiers are shown to reach an Fh-score of up to 0.88.

Several emerging applications for the Internet of Things, vehicular networks, or decentralized communication using smartphones rely on Mobile Ad hoc Networks (MANETs). These networks are temporary deployments of nodes equipped with infrastructure-less wireless communication. MANETs operate in highly dynamic conditions where nodes move at will, interferences are a constant and density is heterogeneous. Routing is a fundamental operations in MANETs. Our evaluation of existing routing protocol for MANETs shows that, while proactive routing protocols are suitable for highly dynamic networks, reactive routing protocols perform best in dense and more static scenarios. No protocol alone can systematically perform well when density is heterogeneous. We propose RoVy, a self-aware adaptive approach for routing in heterogeneous MANETs. Based on independent estimations of density and mobility, RoVy allows nodes to automatically switch between AODV, a reactive routing protocol and DSDV, a proactive protocol. Interoperability protocols support the integration of AODV and DSDV in a single heterogeneous MANET. RoVy maintains a dissemination overlay to speed-up route discovery and improves the emergence of alternative routes to destination nodes. Our simulations of the full network stack with 1,000 nodes shows that RoVy outperforms singular routing protocols in terms of performance, costs and reliability.

The submitted contribution is devoted to the issue of the existence of latent losses on the overhead lines. Quantification of latent losses of electricity is a very complicated task, which has been solved in practice for several years. In their assessment, however, often appear some inconsistencies which cause is often unknown. The magnitude of these losses in some cases cannot be negligible. From the point of view of maintaining an efficient and reliable operation of distribution and transmission systems in the future, attention will still need to be paid to the problem of latent loss analysis.

A hypergraph model for mapping applications with an all-neighbor communication pattern to distributed-memory computers is proposed, which originated in finite element triangulations. Rather than approximating the communication volume for linear algebra operations, this new model represents the communication volume exactly. To this end, a hypergraph partitioning problem is formulated where the objective function involves a new metric. This metric, the @l(@l-1)-metric, accurately models the communication volume for an all-neighbor communication pattern occurring in a concrete finite element application. It is a member of a more general class of metrics, which also contains more widely used metrics, such as the cut-net and the (@l-1)-metric. In addition, we develop a heuristic to minimize the communication volume in the new @l(@l-1)-metric. For the solution of several real-world finite element problems, experimental results based on this new heuristic demonstrate a small reduction in communication volume compared to a standard graph partitioner and do not show significant reductions in communication volume compared to a hypergraph partitioner using the common (@l-1)-metric. However, for this set of problems, the new approach does reduce actual communication times. As a by-product, we observe that it also tends to reduce the number of messages. Furthermore, the new approach dramatically reduces the communication volume for a set of sparse matrix problems that are more irregularly-structured than finite element problems.

Partial shading is widely considered to be a limiting factor in the performance of photovoltaic (PV) systems applied in urban environments. Modern system architectures, combined with per module deployment of power electronics, have been used to improve performance, especially at heterogeneous irradiance conditions, but they come with a high investment cost. In this paper, another approach is used to evaluate the selective deployment of power optimizers (SDPO), which can operate with a variety of string inverters and can be retrofitted in PV systems suffering from high shading losses. A combination of modelling and outdoor field testing showed the benefits and drawbacks of SDPOs in a variety of shading scenarios. Results suggest that there is an energy yield increase of 1–2% on an annual basis compared to that of a reference system. The exact level of increase depends on the shading patterns and combination scenarios used in this paper.

Mobile phone data are a novel data source to generate mobility information from Call Detail Records (CDRs). Although mobile phone data can provide us with valuable insights in human mobility, they often show a biased picture of the traveling population. This research, therefore, focuses on correcting for these biases and suggests a new method to scale mobile phone data to the true traveling population. Moreover, the scaled mobile phone data will be compared to roadside measurements at 100 different locations on Dutch highways. We infer vehicle trips from the mobile phone data and compare the scaled counts with roadside measurements. The results are evaluated for October 2015. The proposed scaling method shows very promising results with near identical vehicle counts from both data sources in terms of monthly, weekly, and hourly vehicle counts. This indicates the scaling method, in combination with mobile phone data, is able to correctly measure traffic intensities on highways, and thereby able to anticipate calibrated human mobility behaviour. Nevertheless, there are still some discrepancies&mdash, for one, during weekends&mdash, calling for more research. This paper serves researchers in the field of mobile phone data by providing a proven method to scale the sample to the population, a crucial step in creating unbiased mobility information.

The majority of photovoltaic (PV) systems in the Netherlands are small scale, and installed on residential and commercial rooftops, where different objects in many cases may lead to the presence of shading and inevitable energy loss. Nevertheless, the energy loss due to expected shadow must be distinguished from the energy loss due to other malfunctions. In this study an algorithmic tool is presented that automates the process of analyzing monitoring data of partially shaded PV systems. The algorithm compares long-term and high-resolution yield data of a partially shaded PV system with the yield data of an unshaded PV system, as reference PV system, and automatically detects the energy loss due to the expected shadow, caused by any surrounding obstacles, and distinguishes it from any additional energy loss due to other malfunctions. This study focuses on PV systems with module-level power electronics (MLPE) since these are mostly used on PV systems on rooftops. Three different cases of shaded MLPE PV systems are presented to illustrate the versatility of the methodology. Furthermore, suggestions for further research are discussed at the end of the paper.

A hierarchical control framework is proposed that enables the provision of flexibility services in power systems through aggregation entities. The focus is on both wholesale energy trade functions, i.e. day-ahead market optimisation, and ancillary services to the operators of the transmission and distribution systems, i.e. provision of automatic frequency restoration reserves and peak shaving services. The control framework is generic, scalable, and is effectively configured to address all those diverse needs originating from the lower level, i.e. the device or the user, to the distribution and transmission levels. The work includes the establishment of procedures to identify and solve possible conflicts between the operators of the transmission and distribution systems when procuring ancillary services with competing objectives. The framework is applied on a case study focusing on an aggregation of residential buildings in the Netherlands which are equipped with photovoltaic installations and battery-based energy storage systems. The annual costs and benefits are calculated by using historical market data, whereas the simulation scenarios address the impact of different levels of generation and demand forecasting errors in the economic performance. The outcome of computer simulations provides an informative insight into the differences between the strategies of aggregator companies, and the potential of the investigated markets. The economic evaluation includes the value estimation of peak shaving services at the distribution level. An increase in procurement costs for such services is expected to stimulate the distribution system operators to invest into grid capacity enhancement.

Transparent, consistent accounting of climate-related energy targets is a fundamental building block to the architecture of international climate agreements. Many of these climate targets focus on the transition of the energy system which has led to the development of various energy transition scenarios. Now that fossil resources are being replaced by non-combustible and renewable energy sources a re-assessment of the applicability of the current set of metrics on which these energy scenarios are based is necessary. Now that energy derived from renewable and non-combustible resources with abundant availability but limited instantaneous availability becomes more prominent the ongoing electrification of the energy system combined with the decarbonisation of the electricity system has caused the current set of energy scenario metrics to become ambiguous. More specifically we show that Total Primary Energy (TPE) and its related indicators, Energy Efficiency (EE), Energy Intensity (EI) as well as the key metric Electricity Generation Capacity (EGC) have become unrepresentative, potentially misleading and for a large part irrelevant. This is problematic as these metrics steer climate policy and investment decisions based on statistical artefacts rather than valid representation of the energy system. This study concludes with a set of requirements on energy scenarios to overcome these problems that will lead energy scenarios to focus on Total Final Consumption within a mix of related energy metrics.

The basic reproduction number ℛ 0 is arguably the most important quantity in infectious disease epidemiology. The next-generation matrix (NGM) is the natural basis for the definition and calculation of ℛ 0 where finitely many different categories of individuals are recognized. We clear up confusion that has been around in the literature concerning the construction of this matrix, specifically for the most frequently used so-called compartmental models. We present a detailed easy recipe for the construction of the NGM from basic ingredients derived directly from the specifications of the model. We show that two related matrices exist which we define to be the NGM with large domain and the NGM with small domain . The three matrices together reflect the range of possibilities encountered in the literature for the characterization of ℛ 0 . We show how they are connected and how their construction follows from the basic model ingredients, and establish that they have the same non-zero eigenvalues, the largest of which is the basic reproduction number ℛ 0 . Although we present formal recipes based on linear algebra, we encourage the construction of the NGM by way of direct epidemiological reasoning, using the clear interpretation of the elements of the NGM and of the model ingredients. We present a selection of examples as a practical guide to our methods. In the appendix we present an elementary but complete proof that ℛ 0 defined as the dominant eigenvalue of the NGM for compartmental systems and the Malthusian parameter r , the real-time exponential growth rate in the early phase of an outbreak, are connected by the properties that ℛ 0 > 1 if and only if r > 0, and ℛ 0 = 1 if and only if r = 0.

The design and operation of integrated multi-energy systems require models that adequately describe the behavior of conversion and storage technologies. Typically, linear conversion performance or fixed data from technology manufacturers are employed, especially for new or advanced technologies. This contribution provides a new modeling framework for electrochemical devices, that bridges first-principles models to their simplified implementation in the optimization routine. First, thermodynamic models are implemented to determine the on/off-design performance and dynamic behavior of different types of fuel cells and of electrolyzers. Then, as such nonlinear models are intractable for use in the optimization of integrated systems, different linear approximations are developed. The proposed strategies for the synthesis of reduced order models are compared to assess the impact of modeling approximations on the optimal design of multi-energy systems including fuel cells and electrolyzers. This allows to determine the most suitable level of detail for modeling the underlying electrochemical technologies from an integrated system perspective. It is found that the approximation methodology affects both the design and operation of the system, with a significant effect on system costs and violation of the thermal energy demand. Finally, the optimization and technology modeling framework is exploited to determine guidelines for the installation of the most suitable fuel cell technology in decentralized multi-energy systems. We show how the installation costs of PEMFC, SOFC and MCFC, their electrical and thermal efficiencies, their conversion dynamics, and the electricity price affect the system design and technology selection., Applied Energy, 221, ISSN:0306-2619, ISSN:1872-9118

This paper presents the results of the analyses of operational performance of small-sized residential PV systems, connected to the grid, in the Netherlands and some other European countries over three consecutive years. Web scraping techniques were employed to collect detailed yield data at high time resolution (5–15 min) from a large number (31,844) of systems with 741 MWp of total capacity, delivering data continuously for at least one year. Annual system yield data was compared from small and medium-sized installations. Cartography and spatial analysis techniques in a geographic information system (GIS) were used to visualize yield and performance ratio, which greatly facilitates the assessment of performance for geographically scattered systems. Variations in yield and performance ratios over the years were observed with higher values in 2015 due to higher irradiation values. The potential of specific yield and performance maps lies in the updating of monitoring databases, quality control of data, and availability of irradiation data. The automatic generation of performance maps could be a trend in future mapping.

The modern urban landscape creates numerous challenges for the deployment of solar Photovoltaic (PV) technology. The large structures that dominate the skyline of every city create compactness, which, in turn, limits the available rooftop area and creates unpredicted shading patterns. The majority of research today relies on modern applications such as geographical information system (GIS) software to evaluate urban morphology; however, this approach is computationally intensive and therefore it is usually limited to a small geographical area. In this paper, we approach this issue from another perspective, utilizing the enormous amount of high resolution PV yield data that is available for the Netherlands. Our results not only correlate performance losses with urban compactness indicators, but they also reveal a significant seasonality effect that can reach 15% in some cases.

One of the most important stages of creating DEMs is the selection of a suitable interpolation algorithm. In this paper I decided to take a look at the most popular methods of data interpolation: Inverse Distance Weighting, Natural Neighbour, Spline, Radial Basis Functions, Local Polynomial and Kriging. As the research area served fragment (20 km2) of the Silesian Upland with diversified relief. I analysed visual effects (3D view and profiles), summarized the basic geomorphometric statistics (heights, local relief, slopes, aspects, curvatures) and an assessment of the vertical accuracy of developed models (RMSE and result conformity) have made. After conducted studies it can be stated, that the best interpolation methods for analyse of the relief are Natural Neighbour and Kriging, because they do not create any artefacts.

Part 4: Data Analysis and Information Retrieval; International audience; Deficient visibility in global supply chains causes significant risks for the customs brokerage practices of freight forwarders. One of the risks that freight forwarders face is that shipping documentation might contain document fraud and is used to declare a shipment. Traditional risk controls are ineffective in this regard since the creation of shipping documentation is uncontrollable by freight forwarders. In this paper, we propose a data mining approach that freight forwarders can use to detect document fraud from supply chain data. More specifically, we learn models that predict the presence of goods on an import declaration based on other declared goods and the trajectory of the shipment. Decision rules are used to produce miscoding alerts and smuggling alerts. Experimental tests show that our approach outperforms the traditional audit strategy in which random declarations are selected for further investigation.

Partial shading has a nonlinear effect on the performance of photovoltaic (PV) modules. Different methods of optimizing energy harvesting under partial shading conditions have been suggested to mitigate this issue. In this paper, a smart PV module architecture is proposed for improvement of shade resilience in a PV module consisting of 60 silicon solar cells, which compensates the current drops caused by partial shading. The architecture consists of groups of series-connected solar cells in parallel to a DC-DC buck converter. The number of cell groups is optimized with respect to cell and converter specifications using a least-squares support vector machine method. A generic model is developed to simulate the behavior of the smart architecture under different shading patterns, using high time resolution irradiance data. In this research the shading patterns are a combination of random and pole shadows. To investigate the shade resilience, results for the smart architecture are compared with an ideal module, and also ordinary series and parallel connected architectures. Although the annual yield for the smart architecture is 79.5% of the yield of an ideal module, we show that the smart architecture outperforms a standard series connected module by 47%, and a parallel architecture by 13.4%.

The leader of todays solar energy revolution is undoubtedly the silicon photovoltaic (PV) module. However, despite the immense progress in efficiency and the phenomenal drop of manufacturing and installation costs the dark blue flat panels have not found widespread use in the modern urban environment. The scarcity of available rooftop space, the high cost of land and the irregular metropolitan skyline have not allowed conventional solar technologies to supply cities with clean energy. Thus, new concepts are being investigated to integrate solar generators into new and existing buildings in the form of facades or windows. Luminescent Solar Concentrators (LSCs) offer a novel approach for the utilization of solar irradiation in the form of transparent glazing systems that have the potential to become functional elements of the building envelope. This paper highlights and compares the most recent technological advances in the field of LSC technology and the contribution of colloidal chemistry with reabsorption-free emitters offering broadband absorption and enhanced stability. Combined with a critical study of the newly emerged LSC applications in various fields this study will also attempt to give a possible glimpse of the near future of transparent solar harvesting devices.

toegang via de HU Bibliotheek op het HU netwerk https://doi.org/10.1002/solr.201600015 From the article: "Abstract Inspired by the works of the Dutch artist Piet Mondrian an Electric Mondrian has been developed built up from luminescent solar concentrator elements. It is based on differently colored square and rectangular elements of standard sizes based on multiples of 15 cm, as this the standard size of the c‐Si cells that are used at the sides of the elements. This paper describes the design requirements and choices that have been made in detail. The design is based on commercially available luminescent concentrator Perspex plates and solar cells. Performance testing showed that at total size of 1 m2 a light‐to‐electric power device efficiency is measured of 0.2%: the Electric Mondrian thus provides ∼2 W in full sun, and two mobile devices can be charged directly or via a built‐in battery. The Electric Mondrian functions as a decorative energy‐harvesting element indoors in the urban environment, and can be marketed as such. " https://www.narcis.nl/publication/RecordID/oai%3Adspace.library.uu.nl%3A1874%2F358617

Luminescent solar concentrators consist of coloured plastic plates that concentrate sunlight to their sides where solar cells are mounted. Since their conception in the 1970s, novel dyes and nanocrystals have being developed for these concentrators, and it is time to show their full spectrum of colorful possibilities. In commemoration of the 100th birthday of the art movement “De Stijl”, of which the Dutch artist Piet Mondrian was a prominent member, van Sark et al. (article no. 1600015) have developed a Mondrian-inspired stained glass look-a-like window that serves as a solar energy harvesting device. It is based on luminescent solar concentrators, provides suffi cient power to charge mobile phones when placed in the windowsill and can also be used as an indoor decorative element. The authors describe in detail the design process of the “Electric Mondrian” and it is envisioned that similar designs will be deployed as building-integrated photovoltaic elements.

The power system requires an additional amount of flexibility to process the large-scale integration of renewable energy sources. Community Energy Storage (CES) is one of the solutions to offer flexibility. In this paper two scenarios of CES ownership are proposed. Firstly, an Energy Arbitrage (EA) scenario is studied where an aggregator aims to minimize costs and CO2-emissions of an energy portfolio. Secondly, an Energy Arbitrage - Peak Shaving (EA-PS) scenario is assessed, which is based on a shared ownership between a Distribution System Operator (DSO) and an aggregator. A multi-objective Mixed Integer Linear Programming (MILP) optimization model is developed to minimize the operation costs and CO2-emissions of a community situated in Cernier (Switzerland), using different battery technologies in the CES system. The results demonstrate a profitable system design for all Lithium-ion-Batteries (LiBs) and the Vanadium Redox Flow Battery (VRFB), for both the EA and EA-PS scenarios. The economic and environmental performance of the EA-PS scenario is slightly worse compared to the EA scenario, due to power boundaries on grid absorption and injection to achieve peak shaving. Overall, the differences between the EA and EA-PS scenarios, in economic and environmental performance, are small. Therefore, the EA-PS is recommended to prevent problematic loads on the distribution transformer. In addition, the Pareto frontiers demonstrate that LiBs perform best on both economic and environmental performance, with the best economic and environmental performance for the Lithium-Nickel-Manganese-Cobalt (NMC-C) battery.

33rd European Photovoltaic Solar Energy Conference and Exhibition; 1950-1953, The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinct it from any additional energy loss due to other malfunctions.

Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph coarsening algorithm and use this to implement a parallel agglomerative clustering heuristic on both the CPU and the GPU. This heuristic is able to generate clusterings in very little time: a modularity 0.996 clustering is obtained from a street network graph with 14 million vertices and 17 million edges in 4.6 seconds on the GPU.

Building Integrated and Building Attached Photovoltaic (BIPV, BAPV) systems may suffer from lower performance than predicted as a result of not considered partial shading. New system architectures have been proposed to optimize performance. The common approach of these new architectures is to track the Maximum Power Point (MPP) of every solar module individually. A simulation model is developed to quantify the benefits and drawbacks of different PV system architectures. The model includes a shading evaluation of the installation with means of 3D modeling, irradiance calculations, PV cell modeling and finally an empirical power conversion model. The energy yield of three leading architectures is confirmed (string inverter, power optimizer, micro inverter) for clear and partial shading conditions by means of an outdoor field test. Results with the irradiance profile of the Netherlands show that the string inverter system outperforms MLPE in 2 out of 3 partial shading scenarios that were evaluated in this study. It is found that the energy yield benefit of MLPE has a seasonal and latitude variation with the highest contribution during winter months. Additionally a study was performed to evaluate the energy yield at different irradiance profiles. Results show that there is a marginal benefit of the micro inverter system at higher irradiance locations when partial shading is present. The analysis method can be used by PV installers and system designer to determine which is the optimal system architecture for maximum energy yield especially when partial shading is present.

This unit provides background on the debate about “object-oriented databases” currently raging in the GIS community. It describes the layer view, in which each geographic variable can be conceptualized as a layer that captures the variable’s variation over the surface of the earth, and the object view, in which the world is conceptualized as empty space littered with various types of objects. Data models for representing each view are discussed in addition to the Computer Science concepts of object orientation, identity, inheritance and encapsulation.

Summary For a remote area or an isolated island, where the grid has not extended, a standalone hybrid energy system can provide cheap and adequate power for local users. However, with the development of society, the load demand will increase and the original system cannot completely meet the load demand. This situation occurs in Xiaojin, Sichuan, China. The existing photovoltaic-pumped hydro storage (PV-PHS) hybrid system in this area as the original system cannot completely meet the load requirements at present. The term ?repowering? aims to maximize the reliability of power supply and the utilization of the PV-PHS hybrid energy system that differs from traditional planning optimization to build all components. The repowering strategy is to integrate wind turbines (WTs) and battery into the original system. For the repowering system, a power management strategy is proposed to determine the operating modes of the PHS and battery. Three objectives, which are minimizing percentage of the demand not supplied, levelized cost of energy, and curtailment rate of renewable energy, are considered in the optimization model. Simulation is conducted by single-objective, biobjective, and triobjective particle swarm optimization (PSO) techniques. For the single-objective optimization, the comparison of PSO and genetic algorithm (GA) is made. For the double-objective optimization, multiobjective PSO (MOPSO) is compared with weighted sum approach (WSA), and fuzzy satisfying method is utilized to find the win-win solution. The results reveal that the repowering strategy can help to achieve maximum reliability of power supply after load demand increases significantly, and the battery plays an important role in such a hybrid system.

Long-lasting periodic sensory stimulation is increasingly used in neuroscience to study, using electroencephalography (EEG), the cortical processes underlying perception in different modalities. This kind of stimulation can elicit synchronized periodic activity at the stimulation frequency in neuronal populations responding to the stimulus, referred to as a steady-state response (SSR). While the frequency analysis of EEG recordings is particularly well suited to capture this activity, it is limited by the intrinsic noisy nature of EEG signals and the low signal-to-noise ratio (SNR) of some responses. This paper compares and adapts spatial filtering methods for periodicity maximization to enhance the SNR of periodic EEG responses, a key condition to generalize their use as a research or clinical tool. This approach uncovers both temporal dynamics and spatial topographic patterns of SSRs, and is validated using EEG data from 15 healthy subjects exposed to periodic cool and warm stimuli.

According to the European Union Third Energy Market package, the roll-out of smart meters in the residential sector can presumably play a key role in reaching the goals of sustainability strategies. However, the deployment of smart meters alone does not necessarily drive residential customers to use energy in a more sustainable manner. Therefore, more attention should be paid to customers energy behavior in order to reach the objectives of the roll-out policy. In this study, we propose an interdisciplinary framework that establishes a behavioral model to identify the main energy-related behavior change requirements necessary to engage residential customers in energy applications. To fulfill the requirements, we first present the technical system architecture that enables energy applications for residential customers. Then, we assess how gamification, which is the employment of game design elements in non-game contexts, can be used to enhance energy applications by driving customer engagement and energy-related behavior change. To do that, the most relevant game design elements are discussed and classified. After that, the expected value streams from using a gamification-based solution for different stakeholders in the energy market are identified. Finally, the study discusses the potential of the proposed framework in different energy applications for residential customers.