Hybrid Grids DEMO
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- PublicationComparison of Black Box Models for Load Profile Generation of District Heating NetworksBlack box modeling is a fast and efficient way of creating models for generating the heat demand of a district heating networks. A sufficient amount of high quality data has to be collected to form the basis for a valid model that can serve as training and test stand for the models. The model parameters and their influence on the heat demand are investigated and a model structure is derived. With this structure, five data mining algorithms, namely Multiple Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbor (k-NN) and Artificial Neural Networks (ANN) are utilized for creating the load models for a small district heating network located in southeast of Austria. Except for LR, all algorithms showed a good performance. They are well suited for that kind of task. K-NN has the best regression score metric with an average MAPE of 13.49 %.
- PublicationParticipation in Energy Transition - Challenges within the Scope of Smart GridsThe inclusion and empowerment of civil society and economy are considered necessary prerequisites for the energy transition that has to be achieved. This contribution shows the process of participation approaches in general as well as a use case within the scope of smart grids in particular. The objective of this use case was to determine the users’ willingness to participate in the respective project. A participation workshop was conducted using qualitative and quantitative methods. Results show that since the participants were informed about the system solution for the first time, concern about cost and security were corollary. We conclude that the workshop served rather as instrument to inform and enlighten private end-users providing a baseline to compare results of further investigations. The forthcoming challenge is to ensure an ongoing participation process to raise acceptance and willingness to participate.
- PublicationKünstliche Neuronale NARX-Modelle zur Wärmelastprognose von NahwärmenetzenIm Kontext des Smart Grid und der hybriden Netzbetrachtung stellen Wärmenetze eine Flexibilität für das Stromnetz bereit, die durch intelligente Nutzung eine bessere Integration der erneuerbaren Energieträger ermöglichen kann. Dafür sind Wärmelastprognosen für das Wärmenetz erforderlich, um prädiktiv die Wärmebereitstellung zu regeln. Die Stärken des NARX-Modelles in Kombination mit Neuronalen Netzen liegen dabei in der Lernfähigkeit und Adaptierbarkeit des Modells. Trotz einer suboptimalen Menge an Trainingsdaten und der geringen Größe des betrachteten Nahwärmenetzes, liefert das Modell gute Prognosewerte in einem Prädiktionshorizont von bis zu 24 Stunden.