Original scientific paper
https://doi.org/10.13044/j.sdewes.d7.0286
Comparison of Data-Driven Thermal Building Models for Model Predictive Control
Gernot Steindl
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
Abstract
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.
Keywords
Data-driven; Black-box model; Gray-box model; Model development; Machine learning.
Hrčak ID:
224129
URI
Publication date:
31.12.2019.
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