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- W4256540727 abstract "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." @default.
- W4256540727 created "2022-05-12" @default.
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- W4256540727 date "2021-08-10" @default.
- W4256540727 modified "2023-09-27" @default.
- W4256540727 title "Benchmarking of data predictive control in a real-life apartment during heating season" @default.
- W4256540727 doi "https://doi.org/10.31224/osf.io/exgyw" @default.
- W4256540727 hasPublicationYear "2021" @default.
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