Model Predictive Control is an energy efficient climate control strategy in buildings. However, the effort associated with physics-based modelling seems to prevent widespread application in residential buildings. Applying machine-learning algorithms on historical data promises efficient generation of predictive models for control. In a recent experimental study, Data Predictive Control based on random forests and linear models outperformed a baseline controller during cooling season. In this paper, the approach is benchmarked against hysteresis control and conventional Model Predictive Control based on an RC-network model during heating season. Data Predictive Control shows promising results in terms of energy consumption and thermal comfort.

Model Predictive Control is an energy effcient climate control strategy in buildings. However, the effort associated with physics-based modelling seems to prevent widespread application in residential buildings. Applying machine learning algorithms on historical data promises efficient generation of predictive models for control. In a recent experimental study, Data Predictive Control based on random forests and linear models outperformed a baseline controller during cooling season. In this paper, the approach is benchmarked against hysteresis control and conventional Model Predictive Control based on an RC-network model during heating season. Data Predictive Control shows promising results in terms of energy consumption and thermal comfort.