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A hybrid approach to thermal building modelling using a combination of Gaussian processes and grey-box models.
- Source :
-
Energy & Buildings . Apr2018, Vol. 165, p56-63. 8p. - Publication Year :
- 2018
-
Abstract
- This paper presents a hybrid building modelling method with a reduced modelling and calibration effort. The method combines a physics-based model, which describes the general behaviour of the system, with a machine learning algorithm trained to correct the physics-based model’s systematic errors. To exemplify the method, a highly simplified grey-box model is used as the physics-based part and a Gaussian process as the machine learning part. It is shown that the hybrid model improves the temperature and energy predictions of the grey-box model while having a lower generalization error than the pure Gaussian process. Specifically, the hybrid approach achieved a day-ahead zone temperature prediction error ca. 0.1 K (RMSE) lower than the grey-box model. As for the energy prediction, the hybrid model obtained an error of 3% compared to 8% for the grey-box model. In comparison to the Gaussian process, the hybrid approach achieved better predictions in all cases. The improvements were especially high when the models were trained with small datasets: 0.7 K in the temperature prediction and 25 percentage points in the energy prediction. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03787788
- Volume :
- 165
- Database :
- Academic Search Index
- Journal :
- Energy & Buildings
- Publication Type :
- Academic Journal
- Accession number :
- 128275488
- Full Text :
- https://doi.org/10.1016/j.enbuild.2018.01.039