Applying Machine Learning to Automate Calibration for Model Predictive Control of Building Energy Systems

Building Simulation Conference proceedings, vol. 16, pp. 900–907

Abstract

About 74 % of model calibrations happen manually. This work presents an automated calibration method. A key aspect of calibration is the identification of dominant model parameters, which for energy conversion systems, e.g. heat pumps, strongly depend on the operating state. Starting from energy monitoring data, we analyze the time series and identify characteristic operating periods. The latter can be a start-up phase, continuous operation or a cool down period etc. Training a decision tree classifier with manually assigned data, we process the entire monitoring data automatically and split the data into period specific subsets. Using the Morris-Method for sensitivity analysis enables a ranking of calibration parameters for each subset. Followed by successive calibrations where each only considers the most dominant model parameters, we tune the model. A cross validation finalizes the process.

Authors 6

1. Thomas Storek Aachen Institute for Energy Efficient Buildings and Indoor Climate E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate , Aachen , Germany

   RWTH Aachen University , E.ON Energy Research Center ,

   Institute for Energy Efficient Buildings and Indoor Climate, Aachen, Germany

   RWTH Aachen University, E.ON Energy Research Center,

2. Asad Esmailzadeh Aachen Institute for Energy Efficient Buildings and Indoor Climate E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate , Aachen , Germany

   RWTH Aachen University , E.ON Energy Research Center ,

   Institute for Energy Efficient Buildings and Indoor Climate, Aachen, Germany

   RWTH Aachen University, E.ON Energy Research Center,

3. Phlipp Mehrfeld Aachen Institute for Energy Efficient Buildings and Indoor Climate E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate , Aachen , Germany

   RWTH Aachen University , E.ON Energy Research Center ,

   Institute for Energy Efficient Buildings and Indoor Climate, Aachen, Germany

   RWTH Aachen University, E.ON Energy Research Center,

4. Markus Schumacher Aachen Institute for Energy Efficient Buildings and Indoor Climate E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate , Aachen , Germany

   RWTH Aachen University , E.ON Energy Research Center ,

   Institute for Energy Efficient Buildings and Indoor Climate, Aachen, Germany

   RWTH Aachen University, E.ON Energy Research Center,

5. Marc Baranski Aachen Institute for Energy Efficient Buildings and Indoor Climate E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate , Aachen , Germany

   RWTH Aachen University , E.ON Energy Research Center ,

   Institute for Energy Efficient Buildings and Indoor Climate, Aachen, Germany

   RWTH Aachen University, E.ON Energy Research Center,

6. Dirk Müller Aachen Institute for Energy Efficient Buildings and Indoor Climate E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate , Aachen , Germany

   RWTH Aachen University , E.ON Energy Research Center ,

   Institute for Energy Efficient Buildings and Indoor Climate, Aachen, Germany

   RWTH Aachen University, E.ON Energy Research Center,