Applying machine learning to automate calibration for model predictive control of building energy systems

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.