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Title: Comparison of Black Box Models for Load Profile Generation of District Heating Networks
Authors: Steindl, Gernot 
Pfeiffer, Christian 
Keywords: District Heating Network;Black Box Model;Österreichische Systematik der Wissenschaftszweige 2012::Naturwissenschaften::Informatik::Informatik::Machine Learning;Heat Load Profile;Simulation;Data Mining
Issue Date: Oct-2017
Source: Proceedings of 12th Conference on Sustianable Development of Energy, Water and Environment Systems
Project: Hybrid Grids DEMO 
Abstract: Black 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 %.
Rights: info:eu-repo/semantics/openAccess
Appears in Collections:Schwerpunkt Energie & Umwelt

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