Open AccessBenchmarking of data predictive control in a real-life apartment during heating season
Author(s) -
Benjamin Huber,
Felix Bünning,
Antoon Decoussemaeker,
Philipp Heer,
Ahmed Aboudonia,
John Lygeros
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2042/1/012024
Subject(s) - model predictive control , benchmarking , predictive modelling , control (management) , computer science , thermal comfort , controller (irrigation) , energy consumption , machine learning , control engineering , engineering , artificial intelligence , meteorology , geography , electrical engineering , marketing , agronomy , business , biology
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.