P328 - EnviroInfo 2022

Permanent URI for this collectionhttps://dl.gi.de/handle/20.500.12116/39388

Authors with most Documents  

Gómez, Jorge Marx
2
Wagner vom Berg, Benjamin
2
Akyol, Ali
1
Appold, Christian
1
Bader, Tobias
1

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Subject: search.filters.subject.image recognition

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  • Conference Paper
    The application of image recognition methods to improve the performance of waste-to-energy plantsplants
    (Gesellschaft für Informatik e.V., 2022) Schwark, Fenja; Garmatter, Henriette; Davila, Maria; Dawel, Lisa; Pehlken, Alexandra; Cyris, Fabian; Scharf, Roland; Wohlgemuth, Volker; Naumann, Stefan; Arndt, Hans-Knud; Behrens, Grit; Höb, Maximilian
    In 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.
  • Conference Paper
    Detection of snow-coverage on PV-modules with images based on CNN-techniques
    (Gesellschaft für Informatik e.V., 2022) Hepp, Dennis; Hempelmann, Sebastian; Behrens, Grit; Friedrich, Werner; Wohlgemuth, Volker; Naumann, Stefan; Arndt, Hans-Knud; Behrens, Grit; Höb, Maximilian
    The 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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