Real-life data-driven model predictive control for building energy systems comparing different machine learning models

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By considering forecasts and exploiting storage effects, model predictive control can achieve significant energy and cost savings in the building sector. However, due to the high individual modeling effort, model predictive control lacks practical applicability. For that reason, data-driven process models, approximating the system behavior based on measurements, have become increasingly popular in recent years. Still, scientific literature lacks consent about the most promising model types and efficient workflows to integrate different machine learning models into a model predictive controller. With this work, we present a workflow to provide efficient model predictive controllers based on measurement data automatically. The main idea is to translate different machine learning models into optimization syntax to enable efficient optimization with full access to gradients. We currently consider artificial neural networks, gaussian process regression, and simple linear regression process models. We use a generic model ontology to automatize the controller generation further and test the methodology on two real-life use cases. The first use case is the application of five office rooms with smart thermostat valves. The second use case is a test hall with an air handling unit and a concrete core activation. Using only two days of initial training data, we deploy controllers based on the different model types for six weeks in the offices and apply online learning to improve the models continuously. We observe only minor differences in controller performance despite the artificial neural networks showing the highest prediction accuracy. The second use case shows that the simple linear models require less controller tuning effort. Thus, for practical applications, we recommend linear regression models.