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https://doi.org/10.13044/j.sdewes.d7.0286

Comparison of Data-Driven Thermal Building Models for Model Predictive Control

Gernot Steindl   ORCID icon orcid.org/0000-0002-9035-9206 ; Institute of Computer Engineering, Treitlstrasse 3, A-1040 Wien, Vienna, Austria
Wolfgang Kastner ; Institute of Computer Engineering, Treitlstrasse 3, A-1040 Wien, Vienna, Austria
Verena Stangl ; University of Applied Sciences Burgenland, Steinamangerstraße 21, A-7423 Pinkafeld, Austria

Puni tekst: engleski, pdf (804 KB) str. 730-742 preuzimanja: 66* citiraj
APA 6th Edition
Steindl, G., Kastner, W. i Stangl, V. (2019). Comparison of Data-Driven Thermal Building Models for Model Predictive Control. Journal of Sustainable Development of Energy, Water and Environment Systems, 7 (4), 730-742. https://doi.org/10.13044/j.sdewes.d7.0286
MLA 8th Edition
Steindl, Gernot, et al. "Comparison of Data-Driven Thermal Building Models for Model Predictive Control." Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 7, br. 4, 2019, str. 730-742. https://doi.org/10.13044/j.sdewes.d7.0286. Citirano 15.07.2020.
Chicago 17th Edition
Steindl, Gernot, Wolfgang Kastner i Verena Stangl. "Comparison of Data-Driven Thermal Building Models for Model Predictive Control." Journal of Sustainable Development of Energy, Water and Environment Systems 7, br. 4 (2019): 730-742. https://doi.org/10.13044/j.sdewes.d7.0286
Harvard
Steindl, G., Kastner, W., i Stangl, V. (2019). 'Comparison of Data-Driven Thermal Building Models for Model Predictive Control', Journal of Sustainable Development of Energy, Water and Environment Systems, 7(4), str. 730-742. https://doi.org/10.13044/j.sdewes.d7.0286
Vancouver
Steindl G, Kastner W, Stangl V. Comparison of Data-Driven Thermal Building Models for Model Predictive Control. Journal of Sustainable Development of Energy, Water and Environment Systems [Internet]. 2019 [pristupljeno 15.07.2020.];7(4):730-742. https://doi.org/10.13044/j.sdewes.d7.0286
IEEE
G. Steindl, W. Kastner i V. Stangl, "Comparison of Data-Driven Thermal Building Models for Model Predictive Control", Journal of Sustainable Development of Energy, Water and Environment Systems, vol.7, br. 4, str. 730-742, 2019. [Online]. https://doi.org/10.13044/j.sdewes.d7.0286

Sažetak
Energy flexible buildings in combination with demand response will play a key role in the future smart grid. To implement control strategies, which enable demand response, like model predictive control, thermal building models are necessary. Therefore, three lumped capacitance models, are compared with a k-Nearest Neighbor regression model.
All models show accurate prediction results, if the operating condition of the building is similar during parameter identification or rather during training and the validation period. Parameter identification of lumped capacitance models is a time-consuming task. Especially for complex lumped capacitance models, the search space for certain parameters has to be reduced to avoid local minima. The investigated k-Nearest Neighbor algorithm has the advantage of easy implementation, very fast training and minimal effort for parameter identification in combination with accurate predictions. But its seasonal dependency is very strong, which can be easily overcome with periodically data update, as it is an instance-based learning algorithm.

Ključne riječi
Data-driven; Black-box model; Gray-box model; Model development; Machine learning.

Hrčak ID: 224129

URI
https://hrcak.srce.hr/224129

Posjeta: 132 *