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- KonferenzbeitragThe application of image recognition methods to improve the performance of waste-to-energy plantsplants(EnviroInfo 2022, 2022) Schwark, Fenja; Garmatter, Henriette; Davila, Maria; Dawel, Lisa; Pehlken, Alexandra; Cyris, Fabian; Scharf, RolandIn this paper, we present an image recognition method to improve the performance of waste-to-energy plants. Thermal treatment of waste in waste-to-energy plants is central for the treatment of municipal solid waste. The heterogeneous nature of municipal solid waste results in a fluctuating lower calorific value to which plant operation must be adapted. Compensating for drastic changes in the lower calorific value is challenging for plant operation and can require short-term interventions. Estimating the lower calorific value prior to the combustion process should reduce the number of short-term interventions. In this work, we propose a process-engineering approach to estimate the lower calorific value of waste as a new application of image recognition in waste-to-energy plants. The method is implemented using videos and sensor data from a case study in a real waste-to-energy plant in Germany.
- KonferenzbeitragOData - Usage of a REST Based API Standard in Web based Environmental Information Systems(EnviroInfo 2022, 2022) Hilbring, Désirée; Becker, Kevin; Markus; Emde, Katharina; van der Schaaf, Hylke; Spandl, Horst; Tauber, MartinaEnvironmental information in existing custom-build environmental information systems is manifold. Sharing this information via Web APIs for diverse client types to fulfil the needs of ongoing digitisation efforts is still a challenge. This paper analyses the open standard OData (Open Data Protocol) as a possible communication protocol between independent servers and clients. Of interest is also the question, if the protocol is not only capable of sharing environmental data between independent systems but also if the information provided via OData is sufficient for directly creating a web-based end user client. The developed prototypical implementation is tested in two environmental applications from different domains: a small data overview for decision makers and the integration of information in an environmental platform.
- KonferenzbeitragUsing Deep Learning for automated birth detection during farrowing(EnviroInfo 2022, 2022) Witte, Jan-Hendrik; Gerberding, Johann; Lensches, Clara; Traulsen, ImkePig livestock farming has been undergoing major structural change for years. The number of animals per farm is constantly increasing, while competition is becoming more intense due to volatile slaughter prices. Sustainable, welfare-oriented livestock farming becomes increasingly difficult under these conditions. Studies have shown that animal-specific birth monitoring of sows can significantly reduce piglet losses. However, continuous monitoring by human staff is inconceivable, which is why systems need to be created that assist farmers in these tasks. For this reason, this paper aims to introduce the first step towards an automated birth monitoring system. The goal is to use deep learning methods from the field of computer vision to enable the detection of individual piglet births based on image data. This information can be used to develop systems that detect the beginning of a birth process, measure the duration of piglet births, and determine the time intervals between piglet births.
- KonferenzbeitragDigital Mobility Services for Communities: Flexible boarding points for campus ridesharing(EnviroInfo 2022, 2022) Gieza, Moritz; Schuster, Thomas; Waidelich, Lukas; Kölmel, BernhardMobility is still characterized by individual transport. Despite changes in recent years, it still influences infrastructure development and results in car-friendly cities. As a result, traffic congestion reveals weaknesses in efficiency and sustainability of this model. This is exacerbated in metropolitan areas with high growth rates and in areas with below-average public transport services. Besides congestion, emission such as pollution and noise are a major problem. In this article, we give explain how this affects communities in general and transport from and to our university campus particularly. We will examine how digital mobility services can extend public and individual transport. We will explore how digital services can promote intermodal transport and lead to more sustainability in mobility. Within that discussion, we present a ridesharing platform and study its influence on directions to and from our campus.
- KonferenzbeitragBITS: A Key Performance Indicators (KPIs) supported approach to assess traffic safety for cyclists at intersections in the Netherlands(EnviroInfo 2022, 2022) Schering, Johannes; Gómez, Jorge MarxTraffic safety is an important factor in the decision process whether people decide to use the bicycle or not. Critical situations that do not lead to an accident are often not reported to the police. To fill this knowledge gap, several regions as the city of Zwolle and the Province of Friesland (Netherlands) have started to detect near accidents at intersections among vehicles and bicycles by 3D camera data to evaluate traffic safety. Four intersections in Friesland and Zwolle were monitored. Different types of intersections (e.g. shared space concept) were considered. Near accidents can be divided into different conflict categories depending on vehicle speed and time to collision (Post-Encroachment Time PET). The preprocessed data including Key Performance Indicators (KPIs) to make cycling safety at the intersections measurable and comparable are provided. Based on the numbers and visualizations, it will be discussed which of the discussed intersections show critical profiles regarding numbers of near accidents, distribution and amount of very critical situations. With the results the intersections can be adjusted to increase traffic safety.Encroachment Time PET). The pre-processed data including relevant Key Performance Indicators (KPIs) to make cycling safety at the intersections measurable and comparable are provided. Based on the numbers and visualizations, it will be discussed which of the dis-cussed intersections show critical profiles regarding the total numbers of near accidents, its distribution and the amount of very critical situations. Based on the results the intersections can be adjusted to increase the safety situation in a city.
- KonferenzbeitragInformation Disclosure in VPP - Information Disclosure by Decentralized Coordination in Virtual Power Plants and District Energy Systems(EnviroInfo 2022, 2022) Bremer, Jörg; Lehnhoff, SebastianGrouping small, hardly predictable, and volatile energy resources to jointly operating virtual power plants with sufficient flexibility for coordination is widely seen as a key aspect of integrating renewable energy into the grid. For several reasons, self-organizing, agent-based systems are probably the best technology for coordination. A major drawback of many currently existing solutions is the necessity to communicate plain information for negotiation and optimization. Such information contains e.g. possible energy generation schemes or aggregated costs. Previous works have already shown that identification of anonymously sent information is possible. In this paper, we demonstrate the possibility of disaggregating cost structure information as an example of possible leakage of business information in the case of participation in virtual power plants or district energysystems. From this perspective, we derive measures to ensure privacy preservation in decentralized coordination algorithms
- KonferenzbeitragAn Artificial Intelligence of Things based Method for Early Detection of Bark Beetle Infested Trees(EnviroInfo 2022, 2022) Knebel, Peter; Appold, Christian; Guldner, Achim; Horbach, Marius; Juncker, Yasmin; Müller, Simon; Matheis, AlfonsBark beetles, like the European Spruce Bark Beetle (Ips typographus), are inherent partsof a forest ecosystem. However, with favorable conditions, they can multiply quickly and infest vastamounts of trees and cause their extinction. Therefore, it is important for forest officials and rangers ofe. g. a national park, to monitor the population of the beetles and the infested trees. There are severalways to approach this, but they are often costly and time-consuming. Therefore, we design and test abark beetle early warning system with AI-based data analysis: Audio data, data on pheromones andinformation for a drought stress assessment of the affected trees are to be collected and used as a basisfor the analysis. The aim is to devise a micro-controller-based sensor system that detects the infestationof a tree as early as possible and warns the forest officials, e. g. via a message on their cell phone.
- KonferenzbeitragEnviroInfo 2022 - Komplettband(EnviroInfo 2022, 2022)
- KonferenzbeitragRemote sensing data analysis via machine learning for land use estimation in the Greater Thessaloniki Area, Greece(EnviroInfo 2022, 2022) Katsalis, Paraskevas; Bagkis, Evangelos; Karatzas, KostasRemote sensing data have been employed for monitoring the differences in land use over time. This information serves as the basis of any further land-related analysis, modelling and decision making. It requires satellite coverage of an area of interest, in various bands, and intense analysis of the data to correctly identify the different land types and associate them to the geographical reality precisely. In this paper, we collect Sentinel 2, level 1C satellite data to extract spectral indices and utilise them as features for land cover classification. The method is based on the use of machine learning for properly mapping the Greater Thessaloniki Area, engaging the random forest algorithm. Two different classification configurations in terms of target labels are tested for their accuracy. The main goal of the study is to present a pipeline for researchers and practitioners that need to define non-generic classes and classify geographical areas accordingly. Results, evaluated with the confusion matrix, suggest excellent performance on the test set and bring to surface limitations of the approach concerning the lack of proper high-quality data for algorithm training.
- KonferenzbeitragDetection of snow-coverage on PV-modules with images based on CNN-techniques(EnviroInfo 2022, 2022) Hepp, Dennis; Hempelmann, Sebastian; Behrens, Grit; Friedrich, WernerThe transition from fossil fuels to renewable energy is considered as very meaningful to mitigate climate change. To integrate weather-dependent energies firmly into the power grid, a forecast of the energy yield is very important. This paper is about renewable energy generation by photovoltaic (PV) systems. The yield of PV-systems depends not only on weather conditions, but in wintertime also on the additional factor “snow cover”. The aim of this work is to detect snow cover on photovoltaic plants to support the energy yield forecast. For this purpose, images of a PV-plant with and without snow cover are used for feature extraction and then analyzed by using a convolutional neural network (CNN).
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