Steindl, GernotGernotSteindlPfeiffer, ChristianChristianPfeiffer0000-0002-1227-84432017-12-192017-12-192017-10Proceedings of 12th Conference on Sustianable Development of Energy, Water and Environment Systemshttp://hdl.handle.net/20.500.11790/1017Black 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 %.application/pdfeninfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/District Heating NetworkBlack Box ModelÖsterreichische Systematik der Wissenschaftszweige 2012::Naturwissenschaften::Informatik::Informatik::Machine LearningHeat Load ProfileSimulationData Mininginfo:eu-repo/semantics/conferenceObject