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Auflistung nach Schlagwort "artificial neural networks"

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  • Konferenzbeitrag
    Training of Artificial Neural Networks Based on Feed-in Time Series of Photovoltaics and Wind Power for Active and Reactive Power Monitoring in Medium-Voltage Grids
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Dipp, Marcel; Menke, Jan-Hendrik; Wende - von Berg, Sebastian; Braun, Martin
    Today, there is already a significant injection of renewable energies at the medium-voltage level, which requires the use of reliable monitoring methods. In addition to tracking electrical parameters such as line current or bus voltage magnitudes, precise knowledge of the active and reactive power feed-in is becoming increasingly relevant in order to provide the necessary information for optimization strategies at higher voltage levels. For this reason, we have developed a method to monitor the active and reactive power for the medium-voltage level with very low measurement density, which is based on artificial neural networks (ANN). The actual training of ANN is accomplished with photovoltaics (PV) and wind feed-in time series based on real weather data to ensure realistic monitoring of the injection. The presented method is applied to a German medium-voltage grid to evaluate the estimation accuracy.
  • Konferenzbeitrag
    Using four data mining techniques to predict grain yield response of winter wheat under organic farming system
    (41. GIL-Jahrestagung, Informations- und Kommunikationstechnologie in kritischen Zeiten, 2021) Halwani, Mosab; Bachinger, Johann
    Organic farming is one of the resource-conserving and environmentally friendly systems that achieve the sustainability principles. An essential issue for sustainable agricultural planning is the accurate yield estimation for the crops involved in the crop rotation. In this study, the potential of predicting grain yield for organic winter wheat under varying soil and climate conditions was conducted by applying four different data mining techniques: multi linear regression (MLR), general linear model (GLM), artificial neural networks (ANN), and regression trees (RT). Considering the modelling accuracy and prediction accuracy, RT is the most robust technique for predicting grain yield of winter wheat at the study sites. MLR and GLM produced the poorest results for the data sets compared in this research. Such poor performance might be due to insufficient MLR and GLM techniques to model non-linear regression present in complex soil-weather-land management interactions. The ANN model ANN is also a promising tool for predicting grain yield of winter wheat particularly under low sample size, however, optimum model structures require further attention.