In December 2018, wastewater was officially recognized by the European Union as a renewable source of energy, thus wastewater heat recovery can be included in efforts to reduce greenhouse gas ...emissions. Given the fact that wastewater treatment plants can generate enormous heat surpluses, this decision gives leeway to couple the wastewater infrastructure with the energy system in order to increase energy efficiency at the system level, allow for power to heat solutions in order to integrate volatile renewable electricity generation and, thus, foster a sustainable energy transition and cleaner production. Yet, the success of these system integrations depends on the availability of energy consumers in proximity of the wastewater treatment plants, and the temporal patterns of energy supply and energy demand. So far, the importance of both temporal and spatial variations in performance of wastewater heat recovery systems have been discussed in literature, but only as separate considerations to date. In order to exhaust the potential of wastewater energy, the combination of both aspects still has to be studied sufficiently, and this paper aims at filling that gap. A three-step methodology is proposed, including an energetic analysis at the wastewater treatment plant, a spatio-temporal analysis of supply and demand in potential supply areas, and an integrated analysis, overlaying the supply and demand profiles. This allows to account for both the proximity of consumers and potential temporal mismatches between supply and demand. The methodology was applied on a case study in Ireland, being able to clearly identify potentials and pitfalls for laying out grids and dimensioning the energy generation systems. It can be concluded, that wastewater energy is a well-suited source of energy to supply baseloads, but the spatio-temporal patterns reveal that both periods of excess wastewater heat potentials as well as additional heating in bivalent systems is required. Therefore, the spatial – urban and regional – fabric, the mix of land uses and their density, largely determine the layout and the useable amount of this renewable energy source. Finally, it can be concluded, that the use of wastewater energy provides feasible and valuable contributions for sustainable urban energy supply systems and cleaner production if the electricity sources for the respective heat pump systems are renewable guaranteeing low-to zero-emission operation.
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•An integrated spatio-temporal analysis of wastewater heat recovery is proposed.•5 zones, within 2 km of the plant, were considered as a heat consumer.•On 21–136 days, the heat supply was insufficient to meet demand.•58 % to 93 % of all demand can be supplied by the heat recovery system.•The recovered heat did not reach its full potential, due to temporal mismatches.
In recent years, many approaches have been developed that efficiently and effectively visualize movement data, e.g., by providing suitable aggregation strategies to reduce visual clutter. Analysts ...can use them to identify distinct movement patterns, such as trajectories with similar direction, form, length, and speed. However, less effort has been spent on finding the semantics behind movements, i.e. why somebody or something is moving. This can be of great value for different applications, such as product usage and consumer analysis, to better understand urban dynamics, and to improve situational awareness. Unfortunately, semantic information often gets lost when data is recorded. Thus, we suggest to enrich trajectory data with POI information using social media services and show how semantic insights can be gained. Furthermore, we show how to handle semantic uncertainties in time and space, which result from noisy, unprecise, and missing data, by introducing a POI decision model in combination with highly interactive visualizations. Finally, we evaluate our approach with two case studies on a large electric scooter data set and test our model on data with known ground truth.
Pluvial flood risk is increasing in urban and rural areas due to changes in precipitation patterns and urbanization. Pluvial flooding is often associated with insufficient capacities of the sewer ...system or low surface drainage efficiency of urban areas. In hilly areas, hillside runoff additionally affects the risk of pluvial flooding. This article introduces a methodical approach and related evaluation criteria for a systematic analysis of potential causes of urban pluvial flooding. In the presented case study, the cause of pluvial flooding at two selected sites in a hillside settlement is investigated based on a coupled 1D/2D model of the whole hydrological catchment. The results show that even though bottlenecks in the sewer system are important, the effect of low surface drainage efficiency and hillside runoff greatly influence pluvial flooding. The knowledge of different causes of flooding can be further used for selecting and positioning appropriate adaption measures. The presented approach proved its practicability and can thus serve as a guidance and template for other applications to gain better understanding and knowledge of local specific pluvial flooding events.
Visual Neural Decomposition to Explain Multivariate Data Sets Knittel, Johannes; Lalama, Andres; Koch, Steffen ...
IEEE transactions on visualization and computer graphics,
2021-Feb., 2021-02-00, 2021-2-00, 20210201, Letnik:
27, Številka:
2
Journal Article
Recenzirano
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Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of ...which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.
Cities worldwide are facing several challenges connected to urbanization and climate change. Several cities have identified the implementation of nature-based solutions (NBS) as an option to mitigate ...several challenges at once. However, can two different aims be reached with NBS in the same location? This question has not yet been addressed. This paper discusses the spatial compatibility of NBS implementation strategies to tackle (1) urban heat island (UHI) effects and (2) water pollution at the same location. The evaluation is based on a spatial analysis of Berlin. We found a positive correlation of high UHI and median high stormwater pollution loads for zinc, total suspended solids, Polycyclic Aromatic Hydrocarbons and Terbutryn. Out of more than 14,000 building/street sections analyzed, 2270 showed spatial matching of high UHI and high stormwater pollution loads. In the majority of building/street sections, stormwater pollution was high for three out of the four parameters. We conclude that the compatibility of NBS implementation for both challenges depends both on the implementation strategies for NBS and on the specific NBS measures. Our spatial analysis can be used for further planning processes for NBS implementation.
Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analyst's information ...need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifier's quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora.
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