Economic Model Predictive Control for District Heating Networks with Forecasting of Uncertain Heat Demand - Data

Description

This dataset compiles the data used to investigate how uncertainties in consumer heat demand in district heating networks affect violations of minimum temperature constraints at consumers. We examine the advantages of employing model predictive control compared to day-ahead optimization. It also contains simulation results and optimization evaluations for the different optimization strategies investigated. The example district heating network data is in the aroma_mpc.zip archive. For demand forecasting, demand data was clustered and models were created. Both are in the Profiles.zip archive. Optimization and simulation results are in the results.zip archive. For the code and a more detailed description, see the linked Git repository.

Steps to reproduce

See the linked git repository.

Institutions

• Fraunhofer Institute for Industrial Mathematics

  Rheinland-Pfalz, Kaiserslautern

Categories

Funders

• Bundesministerium für Forschung, Technologie und Raumfahrt

  North Rhine-Westphalia, Bonn

  Grant ID: 05M22AMA

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